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Min-Ling Zhang

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AAAI Conference 2026 Conference Paper

APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information Retrieval

  • Hong Gao
  • Yiming Bao
  • Xuezhen Tu
  • Bin Zhong
  • Linan Yue
  • Min-Ling Zhang

Current multimodal large language models (MLLMs) struggle with hour-level video understanding, facing significant challenges not only in modeling the substantial information volume of long videos but also in overcoming the memory wall and resource constraints during both training and inference. Although recent training-free approaches have alleviated resource demands by compressing visual features, their reliance on incomplete visual information limits the performance potential. To address these limitations, we propose Adaptive Pivot Visual information Retrieval (APVR), a training-free framework that hierarchically retrieves and retains sufficient and important visual information. It breakthroughs the memory wall limitation via two complementary components: Pivot Frame Retrieval employs query expansion and iterative spatio-semantic confidence scoring to identify relevant video frames, and Pivot Token Retrieval performs query-aware attention-driven token selection within up to 1024 pivot frames. This dual granularity approach enables the processing of hour-long videos while maintaining semantic fidelity. Experimental validations on three different baseline MLLMs demonstrate significant performance improvements up to 9.5%, 4.6% and 9.7% on LongVideoBench, VideoMME and MLVU, respectively. APVR achieves state-of-the-art results for both training-free and training-based approaches.

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AAAI Conference 2026 Conference Paper

Classifier-induced Reciprocal Points for Multi-label Open-set Recognition

  • Yibo Wang
  • Yong Rui
  • Min-Ling Zhang

Multi-label learning is a practical machine learning paradigm dealing with instances associated with multiple labels simultaneously. Most existing multi-label learning studies are designed under the closed-world assumption, i.e. a fixed size of label space. However, it encounters significant difficulties in open-set scenarios, where test data may contain unknown labels absent from the training set to be recognized. Existing method typically tackles this challenging problem through sub-labeling approximations and prototype-based comparisons, which often overlooks the implicit information carried by unknown labels. To address this, we propose a novel framework CREM, i.e. Classifier-induced REciprocal point for Multi-label open-set recognition, which rethinks the above problem from the reciprocal point perspective. Specifically, reciprocal points are formulated by explicitly constraining the opposition feature space to a learnable bounded margin. Then reciprocal points can be induced through the classifier with the instance-wise bias eliminated. Subsequently, a unified optimization framework is introduced to jointly facilitate the classifier and reciprocal points induction. Extensive experiments demonstrate the effectiveness and superiority of the proposed CREM approach in the multi-label open-set recognition paradigm.

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AAAI Conference 2026 Conference Paper

Collaborative Dual Representations for Semi-Supervised Partial Label Learning

  • Wei-Xuan Bao
  • Yong Rui
  • Min-Ling Zhang

Semi-supervised partial label learning (SSPLL) aims to improve the generalization performance of partial label (PL) classifiers by effectively leveraging unlabeled data. Nevertheless, the inherent ambiguity in supervision, where the ground-truth label of a PL example is hidden within a set of candidate labels, poses significant challenges. The presence of false positive labels potentially misleads model's judgment, resulting in pronounced confirmation bias. To address these issues, we propose a novel approach named CODUAL, which jointly learns a pair of dual representations for each instance: the predictive class distribution and the low-dimensional embedding. The dual representations interact and progress collaboratively during training. On one hand, in the embedding space the class prototypes are derived via solving a tailored empirical distance minimization problem and employed to smooth the pseudo-targets of unlabeled instances. On the other hand, the refined class distributions regularize the embedding space via encouraging instances with similar pseudo-targets to exhibit similar embeddings. Through an in-depth analysis, we provide-to the best of our knowledge-the first theoretical explanation of how collaborative dual representations facilitate more effective use of unlabeled data for disambiguation. Extensive experiments over benchmark datasets validate the superiority of our proposed approach.

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NeurIPS Conference 2025 Conference Paper

ComRank: Ranking Loss for Multi-Label Complementary Label Learning

  • Jing-Yi Zhu
  • Yi Gao
  • Miao Xu
  • Min-Ling Zhang

Multi-label complementary label learning (MLCLL) is a weakly supervised paradigm that addresses multi-label learning (MLL) tasks using complementary labels (i. e. , irrelevant labels) instead of relevant labels. Existing methods typically adopt an unbiased risk estimator (URE) under the assumption that complementary labels follow a uniform distribution. However, this assumption fails in real-world scenarios due to instance-specific annotation biases, making URE-based methods ineffective under such conditions. Furthermore, existing methods underutilize label correlations inherent in MLL. To address these limitations, we propose ComRank, a ranking loss framework for MLCLL, which encourages complementary labels to be ranked lower than non-complementary ones, thereby modeling pairwise label relationships. Theoretically, our surrogate loss ensures Bayes consistency under both uniform and biased cases. Experiments demonstrate the effectiveness of our method in MLCLL tasks. The code is available at https: //github. com/JellyJamZhu/ComRank.

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AAAI Conference 2025 Conference Paper

Evolutionary Classifier Chain for Multi-Dimensional Classification

  • Yu-Yang Zhang
  • Bin-Bin Jia
  • Min-Ling Zhang

In multi-dimensional classification (MDC), the classifier chain approach is based on a chain structure to model dependencies between class spaces. However, current research on constructing a chain order is usually based on a greedy criterion or random generation, which is highly likely to lead to an incorrect chain order and fit incorrect class dependencies. Moreover, existing classifier chain-based approaches do not consider the misleading effects of irrelevant input features on the classifiers. To fill the above gap, a classifier chain-based approach incorporating evolutionary chain order optimization and feature selection (ECCO) is proposed. Specifically, this approach designs a meta-heuristic algorithm to optimize the chain order of multiple classifiers. Simultaneously, the approach selects dimension-specific feature combinations that are more conducive to class prediction of each dimension. These strategies enhance the class prediction capability of the constructed MDC model. Comparative experiments on 14 real datasets validate that ECCO outperforms 7 state-of-the-art MDC approaches.

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AAAI Conference 2025 Conference Paper

Fast Multi-Instance Partial-Label Learning

  • Yin-Fang Yang
  • Wei Tang
  • Min-Ling Zhang

Multi-instance partial-label learning (MIPL) is a paradigm where each training example is encapsulated as a multi-instance bag associated with the candidate label set, which includes one true label and several false positives. Current MIPL algorithms typically assume that all instances are independent, thereby neglecting the dependencies and heterogeneity inherent in MIPL data. Moreover, these algorithms often prove to be excessively time-consuming when dealing with complex datasets, significantly limiting the practical application of MIPL. In this paper, we propose FastMIPL, a framework that employs mixed-effects model to explicitly capture the dependencies and heterogeneity among instances and bags. FastMIPL is able to learn from MIPL data both effectively and efficiently by utilizing the predefined dependencies modeling module and leveraging the posterior predictive probability disambiguation strategy. Experiments show that the performance of FastMIPL is highly competitive to state-of-the-art methods, while significantly reducing computational time in benchmark and the real-world datasets.

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ICML Conference 2025 Conference Paper

Generalization Analysis for Controllable Learning

  • Yifan Zhang
  • Xiao Zhang 0034
  • Min-Ling Zhang

Controllability has become a critical issue in trustworthy machine learning, as a controllable learner allows for dynamic model adaptation to task requirements during testing. However, existing research lacks a comprehensive understanding of how to effectively measure and analyze the generalization performance of controllable learning methods. In an attempt to move towards this goal from a generalization perspective, we first establish a unified framework for controllable learning. Then, we develop a novel vector-contraction inequality and derive a tight generalization bound for general controllable learning classes, which is independent of the number of task targets except for logarithmic factors and represents the current best-in-class theoretical result. Furthermore, we derive generalization bounds for two typical controllable learning methods: embedding-based and hypernetwork-based methods. We also upper bound the Rademacher complexities of commonly used control and prediction functions, which serve as modular theoretical components for deriving generalization bounds for specific controllable learning methods in practical applications such as recommender systems. Our theoretical results without strong assumptions provide general theoretical guarantees for controllable learning methods and offer new insights into understanding controllability in machine learning.

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AAAI Conference 2025 Conference Paper

Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning

  • Jun-Xiang Mao
  • Yong Rui
  • Min-Ling Zhang

Multi-label metric learning, as an extension of metric learning to multi-label scenarios, aims to learn better similarity metrics for objects with rich semantics. Existing multi-label metric learning approaches employ the common assumption of equal labeling-importance, i.e., all associated labels are considered relevant to the training instance, while there is no differentiation in the relative importance of their semantics. However, this common assumption does not reflect the fact that the importance of each relevant label is generally different, even though such importance information is not directly accessible from the training examples. In this paper, we claim that it is beneficial to leverage the implicit Relative LabelingImportance (RLI) information to facilitate multi-label metric learning. Specifically, the manifold structure within the feature space is exploited by local linear reconstruction, and then the RLIs are recovered by transferring such structure to the label space. Subsequently, a discrimiative multi-label metric learning framework is introduced to align the predictive modeling outputs with the recovered RLIs, under which instances with similar RLI are implicitly pulled closer to each other, while those with dissimilar RLI are pushed further apart. Comprehensive experiments on benchmark multi-label datasets validate the superiority of our proposed approach in learning effective similarity metrics between multi-label examples.

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ICML Conference 2025 Conference Paper

LADA: Scalable Label-Specific CLIP Adapter for Continual Learning

  • Mao-Lin Luo
  • Zi-Hao Zhou
  • Tong Wei 0001
  • Min-Ling Zhang

Continual learning with vision-language models like CLIP offers a pathway toward scalable machine learning systems by leveraging its transferable representations. Existing CLIP-based methods adapt the pre-trained image encoder by adding multiple sets of learnable parameters, with each task using a partial set of parameters. This requires selecting the expected parameters for input images during inference, which is prone to error that degrades performance. To address this problem, we introduce LADA ( L abel-specific ADA pter). Instead of partitioning parameters across tasks, LADA appends lightweight, label-specific memory units to the frozen CLIP image encoder, enabling discriminative feature generation by aggregating task-agnostic knowledge. To prevent catastrophic forgetting, LADA employs feature distillation for seen classes, preventing their features from being interfered with by new classes. Positioned after the image encoder, LADA prevents gradient flow to the frozen CLIP parameters, ensuring efficient training. Extensive results show that LADA achieves state-of-the-art performance in continual learning settings. The implementation code is available at https: //github. com/MaolinLuo/LADA.

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ICML Conference 2025 Conference Paper

Learnware Specification via Dual Alignment

  • Wei Chen 0141
  • Junxiang Mao
  • Xiaozheng Wang
  • Min-Ling Zhang

The learnware paradigm aims to establish a learnware dock system that contains numerous leanwares, each consisting of a well-trained model and a specification, enabling users to reuse high-performing models for their tasks instead of training from scratch. The specification, as a unique characterization of the model’s specialties, dominates the effectiveness of model reuse. Existing specification methods mainly employ distribution alignment to generate specifications. However, this approach overlooks the model’s discriminative performance, hindering an adequate specialty characterization. In this paper, we claim that it is beneficial to incorporate such discriminative performance for high-quality specification generation. Accordingly, a novel specification approach named Dali, i. e. , Learnware Specification via Dual ALIgnment, is proposed. In Dali, the characterization of the model’s discriminative performance is modeled as discriminative alignment, which is considered along with distribution alignment in the specification generation process. Theoretical and empirical analyses clearly demonstrate that the proposed approach is capable of facilitating model reuse in the learnware paradigm with high-quality specification generation.

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AAAI Conference 2025 Conference Paper

Learnware Specification via Label-Aware Neural Embedding

  • Wei Chen
  • Jun-Xiang Mao
  • Min-Ling Zhang

The learnware paradigm aims to establish a learnware dock system of numerous well-trained machine learning models, enabling users to reuse existing helpful models for their tasks instead of starting from scratch. Each learnware in the system is a well-established model submitted by its developer, associated with a specification generated by the learnware dock system. The specification characterizes the specialty of the corresponding model, enabling it to be identified accurately for new task requirements. Existing specification generation methods are mostly based on the Reduced Kernel Mean Embedding (RKME) technique, which uses the Maximum Mean Discrepancy (MMD) in the Reproducing Kernel Hilbert Space (RKHS) to seek a reduced set that characterizes the model's capabilities. However, existing RKME-based methods mainly utilize feature information to generate specifications by assuming the existence of the ground-truth labeling function, while leaving the label information, which is capable of providing rich semantic characterization, untouched. Furthermore, the quality of the generated specifications heavily relies on the choice of the kernels, which makes it prohibitive to adapt to all real-world scenarios. In this paper, to overcome the above limitations, we propose a novel specification approach named LANE, i.e., Label-Aware Neural Embedding. In LANE, the neural embedding space is utilized to replace the RKHS, effectively circumventing the step of kernel selection and thereby addressing the dependency on kernels in existing RKME-based specification methods. More importantly, LANE uses the label information as additional supervision to enhance the generation process, resulting in specifications of superior quality. Extensive experiments demonstrate the effectiveness and superiority of the proposed LANE approach in the learnware paradigm.

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ICLR Conference 2025 Conference Paper

Noise Separation guided Candidate Label Reconstruction for Noisy Partial Label Learning

  • Xiaorui Peng
  • Yuheng Jia
  • Fuchao Yang
  • Ran Wang 0001
  • Min-Ling Zhang

Partial label learning is a weakly supervised learning problem in which an instance is annotated with a set of candidate labels, among which only one is the correct label. However, in practice the correct label is not always in the candidate label set, leading to the noisy partial label learning (NPLL) problem. In this paper, we theoretically prove that the generalization error of the classifier constructed under NPLL paradigm is bounded by the noise rate and the average length of the candidate label set. Motivated by the theoretical guide, we propose a novel NPLL framework that can separate the noisy samples from the normal samples to reduce the noise rate and reconstruct the shorter candidate label sets for both of them. Extensive experiments on multiple benchmark datasets confirm the efficacy of the proposed method in addressing NPLL. For example, on CIFAR100 dataset with severe noise, our method improves the classification accuracy of the state-of-the-art one by 11.57%. The code is available at: https://github.com/pruirui/PLRC.

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AAAI Conference 2025 Conference Paper

Partial Label Causal Representation Learning for Instance-Dependent Supervision and Domain Generalization

  • Yizhi Wang
  • Weijia Zhang
  • Min-Ling Zhang

Partial label learning (PLL) addresses situations where each training example is associated with a set of candidate labels, among which only one corresponds to the true class label. As the candidate labels often come from crowdsourced workers, their generation is inherently dependent on the features of the instance. Existing PLL methods primarily aim to resolve these ambiguous labels to enhance classification accuracy, overlooking the opportunity to use this feature dependency for causal representation learning. This focus on accuracy can make PLL systems vulnerable to stylistic variations and shifts in domain. In this paper, we explore the learning of causal representations within an instance-dependent PLL framework, introducing a new approach that uncovers identifiable latent representations. By separating content from style in the identified causal representation, we introduce CausalPLL+, an algorithm for instance-dependent PLL based on causal representation. Our algorithm performs exceptionally well in terms of both classification accuracy and generalization robustness. Qualitative and quantitative experiments on instance-dependent PLL benchmarks and domain generalization tasks verify the effectiveness of our approach.

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ICLR Conference 2025 Conference Paper

Realistic Evaluation of Deep Partial-Label Learning Algorithms

  • Wei Wang 0373
  • Dong-Dong Wu
  • Jindong Wang 0001
  • Gang Niu 0001
  • Min-Ling Zhang
  • Masashi Sugiyama

Partial-label learning (PLL) is a weakly supervised learning problem in which each example is associated with multiple candidate labels and only one is the true label. In recent years, many deep PLL algorithms have been developed to improve model performance. However, we find that some early developed algorithms are often underestimated and can outperform many later algorithms with complicated designs. In this paper, we delve into the empirical perspective of PLL and identify several critical but previously overlooked issues. First, model selection for PLL is non-trivial, but has never been systematically studied. Second, the experimental settings are highly inconsistent, making it difficult to evaluate the effectiveness of the algorithms. Third, there is a lack of real-world image datasets that can be compatible with modern network architectures. Based on these findings, we propose PLENCH, the first Partial-Label learning bENCHmark to systematically compare state-of-the-art deep PLL algorithms. We investigate the model selection problem for PLL for the first time, and propose novel model selection criteria with theoretical guarantees. We also create Partial-Label CIFAR-10 (PLCIFAR10), an image dataset of human-annotated partial labels collected from Amazon Mechanical Turk, to provide a testbed for evaluating the performance of PLL algorithms in more realistic scenarios. Researchers can quickly and conveniently perform a comprehensive and fair evaluation and verify the effectiveness of newly developed algorithms based on PLENCH. We hope that PLENCH will facilitate standardized, fair, and practical evaluation of PLL algorithms in the future.

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ICLR Conference 2025 Conference Paper

Semi-Supervised CLIP Adaptation by Enforcing Semantic and Trapezoidal Consistency

  • Kai Gan
  • Bo Ye
  • Min-Ling Zhang
  • Tong Wei 0001

Vision-language pre-training models, such as CLIP, have demonstrated strong capability in rapidly adapting to downstream tasks through fine-tuning, and have been widely applied across various tasks. However, when the downstream tasks are constrained by limited image-text paired data, CLIP struggles to effectively address the domain gap between the pre-training and the target tasks. To address this limitation, we propose a novel semi-supervised CLIP training method coined SemiCLIP that leverages a small amount of image-text pairs alongside a large volume of images without text descriptions to enhance CLIP’s cross-modal alignment. To effectively utilize unlabeled images, we introduce semantic concept mining to improve task-specific visual representations by matching images with relevant concepts mined from labeled data. Leveraging matched semantic concepts, we construct learnable surrogate captions for unlabeled images and optimize a trapezoidal consistency to regulate the geometric structure of image-text pairs in the representation space. Experimental results demonstrate that our approach significantly improves the adaptability of CLIP in target tasks with limited labeled data, achieving gains ranging from 1.72\% -- 6.58\% for zero-shot classification accuracy and 2.32\% -- 3.23\% for image-text retrieval performance on standard benchmarks. The source code is available at https://github.com/Gank0078/SemiCLIP.

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ICML Conference 2025 Conference Paper

Tight and Fast Bounds for Multi-Label Learning

  • Yifan Zhang
  • Min-Ling Zhang

Commonly used evaluation metrics in multi-label learning all involve base loss functions, and the theoretical guarantees of multi-label learning often rely on the properties of base loss functions. Some recent theoretical works have used the Lipschitz continuity of base loss functions to prove the generalization bounds for multi-label learning, but the impact of the smoothness of base loss functions on the generalization bounds is completely unknown. In an attempt to make up for this gap in the generalization theory of multi-label learning, we develop some novel vector-contraction inequalities for smooth base loss functions and derive tight generalization bounds with no dependency on the number of labels, up to logarithmic terms. We then exploit local Rademacher complexity to develop some novel local vector-contraction inequalities for smooth base loss functions, which induce generalization bounds with a tighter dependency on the number of labels and a faster convergence rate with respect to the number of examples. In addition, we derive tight generalization bounds with no dependency on the number of labels, up to logarithmic terms, for Macro-Averaged AUC by exploiting the Lipschitz continuity and smoothness of base loss functions, respectively. Our state-of-the-art theoretical results provide general theoretical guarantees for the generalization of multi-label learning.

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ICML Conference 2025 Conference Paper

Towards Escaping from Class Dependency Modeling for Multi-Dimensional Classification

  • Teng Huang 0003
  • Bin-Bin Jia 0001
  • Min-Ling Zhang

In multi-dimensional classification (MDC), the semantics of objects are characterized by multiple class variables from different dimensions. Existing MDC approaches focus on designing effective class dependency modeling strategies to enhance classification performance. However, the intercoupling of multiple class variables poses a significant challenge to the precise modeling of class dependencies. In this paper, we make the first attempt towards escaping from class dependency modeling for addressing MDC problems. Accordingly, a novel MDC approach named DCOM is proposed by decoupling the interactions of different dimensions in MDC. Specifically, DCOM endeavors to identify a latent factor that encapsulates the most salient and critical feature information. This factor will facilitate partial conditional independence among class variables conditioned on both the original feature vector and the learned latent embedding. Once the conditional independence is established, classification models can be readily induced by employing simple neural networks on each dimension. Extensive experiments conducted on benchmark data sets demonstrate that DCOM outperforms other state-of-the-art MDC approaches.

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ICML Conference 2025 Conference Paper

Weakly-Supervised Contrastive Learning for Imprecise Class Labels

  • Zi-Hao Zhou
  • Junjie Wang
  • Tong Wei 0001
  • Min-Ling Zhang

Contrastive learning has achieved remarkable success in learning effective representations, with supervised contrastive learning often outperforming self-supervised approaches. However, in real-world scenarios, data annotations are often ambiguous or inaccurate, meaning that class labels may not reliably indicate whether two examples belong to the same class. This limitation restricts the applicability of supervised contrastive learning. To address this challenge, we introduce the concept of “continuous semantic similarity” to define positive and negative pairs. Instead of directly relying on imprecise class labels, we measure the semantic similarity between example pairs, which quantifies how closely they belong to the same category by iteratively refining weak supervisory signals. Based on this concept, we propose a graph-theoretic framework for weakly-supervised contrastive learning, where semantic similarity serves as the graph weights. Our framework is highly versatile and can be applied to many weakly-supervised learning scenarios. We demonstrate its effectiveness through experiments in two common settings, i. e. , noisy label and partial label learning, where existing methods can be easily integrated to significantly improve performance. Theoretically, we establish an error bound for our approach, showing that it can approximate supervised contrastive learning under mild conditions. The implementation code is available at https: //github. com/Speechless-10308/WSC.

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IJCAI Conference 2025 Conference Paper

Wrapped Partial Label Dimensionality Reduction via Dependence Maximization

  • Xiang-Ru Yu
  • Deng-Bao Wang
  • Min-Ling Zhang

Partial label learning induces classifier from data with ambiguous supervision, where each instance is associated with a set of candidate labels but only one of which is valid. As a classic data preprocessing strategy, dimensionality reduction contributes to enhance the generalization capabilities of learning algorithms. Due to the ambiguity of supervision, existing works on partial label dimensionality reduction are confined to two separate stages: dimensionality reduction and partial label disambiguation. However, the decoupling of dimensionality reduction from partial label disambiguation can lead to severe performance degradation. In this paper, we present a novel approach called Wrapped Partial Label Dimensionality Reduction (WPLDR) to address this challenge. Specifically, WPLDR integrates the dimensionality reduction and partial label disambiguation within a unified framework, employing alternating optimization to concurrently perform dimensionality reduction and partial label disambiguation. WPLDR maximizes the interdependence between features in the embedded space and confidence-based label information, while simultaneously ensuring the manifold consistency between the embedded feature space and label space. Extensive experiments over a broad range of synthetic and real-world partial label data sets validate that the performance of well-established partial label learning algorithms can be significantly improved by the proposed WPLDR.

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NeurIPS Conference 2025 Conference Paper

X-Mahalanobis: Transformer Feature Mixing for Reliable OOD Detection

  • Tong Wei
  • Bolin Wang
  • Jiang-Xin Shi
  • Yu-Feng Li
  • Min-Ling Zhang

Recognizing out-of-distribution (OOD) samples is essential for deploying robust machine learning systems in open-world environments. While conventional OOD detection approaches rely on feature representations from the penultimate layer of neural networks, they often overlook informative signals embedded in intermediate layers. In this paper, we present a straightforward feature mixing approach for pre-trained Transformers, which combines multi-layer representations via calculated importance weights, and identifies OOD samples using Mahalanobis distance in the blended feature space. When in-distribution samples are accessible, we show that parameter-efficient fine-tuning strategies effectively balance classification accuracy and OOD detection performance. We conduct extensive empirical analyses to validate the superiority of our proposed method under zero-shot, and fine-tuning settings using both class-balanced and long-tailed datasets. The source code is available at https: //github. com/SEUML/X-Maha.

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ICML Conference 2024 Conference Paper

Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning

  • Jun-Yi Hang
  • Min-Ling Zhang

Semi-supervised learning (SSL) is a classical machine learning paradigm dealing with labeled and unlabeled data. However, it often suffers performance degradation in real-world open-set scenarios, where unlabeled data contains outliers from novel categories that do not appear in labeled data. Existing studies commonly tackle this challenging open-set SSL problem with detect-and-filter strategy, which attempts to purify unlabeled data by detecting and filtering outliers. In this paper, we propose a novel binary decomposition strategy, which refrains from error-prone procedure of outlier detection by directly transforming the original open-set SSL problem into a number of standard binary SSL problems. Accordingly, a concise yet effective approach named BDMatch is presented. BDMatch confronts two attendant issues brought by binary decomposition, i. e. class-imbalance and representation-compromise, with adaptive logit adjustment and label-specific feature learning respectively. Comprehensive experiments on diversified benchmarks clearly validate the superiority of BDMatch as well as the effectiveness of our binary decomposition strategy.

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IJCAI Conference 2024 Conference Paper

Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning

  • Bo Ye
  • Kai Gan
  • Tong Wei
  • Min-Ling Zhang

In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data. The central challenge is the substantial learning gap between seen and novel categories, as the model learns the former faster due to accurate supervisory information. Moreover, capturing the semantics of unlabeled novel category samples is also challenging due to the missing label information. To address the above issues, we introduce 1) the adaptive synchronizing marginal loss which imposes class-specific negative margins to alleviate the model bias towards seen classes, and 2) the pseudo-label contrastive clustering which exploits pseudo-labels predicted by the model to group unlabeled data from the same category together in the output space. Extensive experiments on benchmark datasets demonstrate that previous approaches may significantly hinder novel class learning, whereas our method strikingly balances the learning pace between seen and novel classes, achieving a remarkable 3% average accuracy increase on the ImageNet dataset. Importantly, we find that fine-tuning the self-supervised pre-trained model significantly boosts the performance, which is overlooked in prior literature. Our code is available at https: //github. com/yebo0216best/LPS-main.

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ICML Conference 2024 Conference Paper

Calibration Bottleneck: Over-compressed Representations are Less Calibratable

  • Deng-Bao Wang
  • Min-Ling Zhang

Although deep neural networks have achieved remarkable success, they often exhibit a significant deficiency in reliable uncertainty calibration. This paper focus on model calibratability, which assesses how amenable a model is to be well recalibrated post-hoc. We find that the widely used weight decay regularizer detrimentally affects model calibratability, subsequently leading to a decline in final calibration performance after post-hoc calibration. To identify the underlying causes leading to poor calibratability, we delve into the calibratability of intermediate features across the hidden layers. We observe a U-shaped trend in the calibratability of intermediate features from the bottom to the top layers, which indicates that over-compression of the top representation layers significantly hinders model calibratability. Based on the observations, this paper introduces a weak classifier hypothesis, i. e. , given a weak classification head that has not been over-trained, the representation module can be better learned to produce more calibratable features. Consequently, we propose a progressively layer-peeled training (PLP) method to exploit this hypothesis, thereby enhancing model calibratability. Our comparative experiments show the effectiveness of our method, which improves model calibration and also yields competitive predictive performance.

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NeurIPS Conference 2024 Conference Paper

Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition

  • Zi-Hao Zhou
  • Siyuan Fang
  • Zi-Jing Zhou
  • Tong Wei
  • Yuanyu Wan
  • Min-Ling Zhang

Long-tailed semi-supervised learning poses a significant challenge in training models with limited labeled data exhibiting a long-tailed label distribution. Current state-of-the-art LTSSL approaches heavily rely on high-quality pseudo-labels for large-scale unlabeled data. However, these methods often neglect the impact of representations learned by the neural network and struggle with real-world unlabeled data, which typically follows a different distribution than labeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. Our framework derives the class-balanced contrastive loss through Gaussian kernel density estimation. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels. By progressively estimating the underlying label distribution and optimizing its alignment with model predictions, we tackle the diverse distribution of unlabeled data in real-world scenarios. Extensive experiments across multiple datasets with varying unlabeled data distributions demonstrate that CCL consistently outperforms prior state-of-the-art methods, achieving over 4% improvement on the ImageNet-127 dataset. The supplementary material includes the source code for reproducibility.

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IJCAI Conference 2024 Conference Paper

Deep Multi-Dimensional Classification with Pairwise Dimension-Specific Features

  • Teng Huang
  • Bin-Bin Jia
  • Min-Ling Zhang

In multi-dimensional classification (MDC), each instance is associated with multiple class variables characterizing the semantics of objects from different dimensions. To consider the dependencies among class variables and the specific characteristics contained in different semantic dimensions, a novel deep MDC approach named PIST is proposed to jointly deal with the two issues via learning pairwise dimension-specific features. Specifically, PIST conducts pairwise grouping to model the dependencies between each pair of class variables, which are more reliable with limited training samples. For extracting pairwise dimension-specific features, PIST weights the feature embedding with a feature importance vector, which is learned via utilizing a global loss measurement based on intra-class and inter-class covariance. Final prediction w. r. t. each dimension is determined by combining the joint probabilities related to this dimension. Comparative studies with eleven real-world MDC data sets clearly validate the effectiveness of the proposed approach.

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AAAI Conference 2024 Conference Paper

Disentangled Partial Label Learning

  • Wei-Xuan Bao
  • Yong Rui
  • Min-Ling Zhang

Partial label learning (PLL) induces a multi-class classifier from training examples each associated with a set of candidate labels, among which only one is valid. The formation of real-world data typically arises from heterogeneous entanglement of series latent explanatory factors, which are considered intrinsic properties for discriminating between different patterns. Though learning disentangled representation is expected to facilitate label disambiguation for partial-label (PL) examples, few existing works were dedicated to addressing this issue. In this paper, we make the first attempt towards disentangled PLL and propose a novel approach named TERIAL, which makes predictions according to derived disentangled representation of instances and label embeddings. The TERIAL approach formulates the PL examples as an undirected bipartite graph where instances are only connected with their candidate labels, and employs a tailored neighborhood routing mechanism to yield disentangled representation of nodes in the graph. Specifically, the proposed routing mechanism progressively infers the explanatory factors that contribute to the edge between adjacent nodes and augments the representation of the central node with factor-aware embedding information propagated from specific neighbors simultaneously via iteratively analyzing the promising subspace clusters formed by the node and its neighbors. The estimated labeling confidence matrix is also introduced to accommodate unreliable links owing to the inherent ambiguity of PLL. Moreover, we theoretically prove that the neighborhood routing mechanism will converge to the point estimate that maximizes the marginal likelihood of observed PL training examples. Comprehensive experiments over various datasets demonstrate that our approach outperforms the state-of-the-art counterparts.

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AAAI Conference 2024 Conference Paper

Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning

  • Dong-Dong Wu
  • Deng-Bao Wang
  • Min-Ling Zhang

Partial label learning (PLL) refers to the classification task where each training instance is ambiguously annotated with a set of candidate labels. Despite substantial advancements in tackling this challenge, limited attention has been devoted to a more specific and realistic setting, denoted as instance-dependent partial label learning (IDPLL). Within this contex, the assignment of partial labels depends on the distinct features of individual instances, rather than being random. In this paper, we initiate an exploration into a self-distillation framework for this problem, driven by the proven effectiveness and stability of this framework. Nonetheless, a crucial shortfall is identified: the foundational assumption central to IDPLL, involving what we term as partial label knowledge stipulating that candidate labels should exhibit superior confidence compared to non-candidates, is not fully upheld within the distillation process. To address this challenge, we introduce DIRK, a novel distillation approach that leverages a rectification process to DIstill Reliable Knowledge, while concurrently preserves informative fine-grained label confidence. In addition, to harness the rectified confidence to its fullest potential, we propose a knowledge-based representation refinement module, seamlessly integrated into the DIRK framework. This module effectively transmits the essence of similarity knowledge from the label space to the feature space, thereby amplifying representation learning and subsequently engendering marked improvements in model performance. Experiments and analysis on multiple datasets validate the rationality and superiority of our proposed approach.

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AAAI Conference 2024 Conference Paper

EAT: Towards Long-Tailed Out-of-Distribution Detection

  • Tong Wei
  • Bo-Lin Wang
  • Min-Ling Zhang

Despite recent advancements in out-of-distribution (OOD) detection, most current studies assume a class-balanced in-distribution training dataset, which is rarely the case in real-world scenarios. This paper addresses the challenging task of long-tailed OOD detection, where the in-distribution data follows a long-tailed class distribution. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes. To overcome this issue, we propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes. This approach allows us to build a detector with clear decision boundaries by training on OOD data using virtual labels. (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data. This technique encourages the model to pay more attention to the discriminative features of the tail classes. We provide a clue for separating in-distribution and OOD data by analyzing gradient noise. Through extensive experiments, we demonstrate that our method outperforms the current state-of-the-art on various benchmark datasets. Moreover, our method can be used as an add-on for existing long-tail learning approaches, significantly enhancing their OOD detection performance. Code is available at: https://github.com/Stomach-ache/Long-Tailed-OOD-Detection.

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IJCAI Conference 2024 Conference Paper

Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning

  • Wei Tang
  • Weijia Zhang
  • Min-Ling Zhang

Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing one true label and several false positives. Existing MIPL algorithms have primarily focused on mapping multi-instance bags to candidate label sets for disambiguation, disregarding the intrinsic properties of the label space and the supervised information provided by non-candidate label sets. In this paper, we propose an algorithm named ELIMIPL, i. e. , Exploiting conjugate Label Information for Multi-Instance Partial-Label learning, which exploits the conjugate label information to improve the disambiguation performance. To achieve this, we extract the label information embedded in both candidate and non-candidate label sets, incorporating the intrinsic properties of the label space. Experimental results obtained from benchmark and real-world datasets demonstrate the superiority of the proposed ELIMIPL over existing MIPL algorithms and other well-established partial-label learning algorithms.

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NeurIPS Conference 2024 Conference Paper

Generalization Analysis for Label-Specific Representation Learning

  • Yi-Fan Zhang
  • Min-Ling Zhang

Label-specific representation learning (LSRL), i. e. , constructing the representation with specific discriminative properties for each class label, is an effective strategy to improve the performance of multi-label learning. However, the generalization analysis of LSRL is still in its infancy. The existing theory bounds for multi-label learning, which preserve the coupling among different components, are invalid for LSRL. In an attempt to overcome this challenge and make up for the gap in the generalization theory of LSRL, we develop a novel vector-contraction inequality and derive the generalization bound for general function class of LSRL with a weaker dependency on the number of labels than the state of the art. In addition, we derive generalization bounds for typical LSRL methods, and these theoretical results reveal the impact of different label-specific representations on generalization analysis. The mild bounds without strong assumptions explain the good generalization ability of LSRL.

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ICML Conference 2024 Conference Paper

Generalization Analysis for Multi-Label Learning

  • Yifan Zhang
  • Min-Ling Zhang

Despite great advances in algorithms for multi-label learning, research on the theoretical analysis of generalization is still in the early stage. Some recent theoretical results has investigated the generalization performance of multi-label learning under several evaluation metrics, however, how to reduce the dependency on the number of labels, explicitly introduce label correlations, and quantitatively analyze the impact of various inductive biases in the generalization analysis of multi-label learning is still a crucial and open problem. In an attempt to make up for the gap in the generalization theory of multi-label learning, we develop several novel vector-contraction inequalities, which exploit the Lipschitz continuity of loss functions, and derive generalization bounds with a weaker dependency on the number of labels than the state of the art in the case of decoupling the relationship among different components, which serves as theoretical guarantees for the generalization of multi-label learning. In addition, we derive the generalization bound for Macro-Averaged AUC and analyze its relationship with class-imbalance. The mild bounds without strong assumptions explain the good generalization ability of multi-label learning with first-order label correlations and high-order label correlations induced by norm regularizers.

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ICML Conference 2024 Conference Paper

Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition

  • Tong Wei 0001
  • Zhen Mao
  • Zi-Hao Zhou
  • Yuanyu Wan
  • Min-Ling Zhang

Long-tail learning primarily focuses on mitigating the label distribution shift between long-tailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing long-tail learning approaches, consistently improving their performance. The source code is available at https: //github. com/Stomach-ache/label-shift-correction

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IJCAI Conference 2024 Conference Paper

Learning Label-Specific Multiple Local Metrics for Multi-Label Classification

  • Jun-Xiang Mao
  • Jun-Yi Hang
  • Min-Ling Zhang

Multi-label metric learning serve as an effective strategy to facilitate multi-label classification, aiming to learn better similarity metrics from multi-label examples. Existing multi-label metric learning approaches learn consistent metrics across all multi-label instances in the label space. However, such consistent metric learning approaches are suboptimal as they neglect the nonlinear distribution characteristics of multi-label instances. In this paper, we present LSMM, a label-specific multi-metric learning framework for multi-label classification, where nonlinear distribution characteristics of multi-label examples are considered by learning label-specific multiple local metrics for different instances on the shoulder of a global one. Specifically, multi-label instances within each label space can be divided into several disjoint partitions through either semantic-based or cluster-based partition strategies, in each of which a local metric is trained to separate the instances locally. Besides, a global metric is introduced to implicitly exploit high-order label correlations across all labels. The combination of the global metric and label-specific local metrics is utilized to measure the semantic similarities between multi-label instances in each label space, under which similar intra-class instances are pushed closer and inter-class instances are pulled apart. Comprehensive experiments on benchmark multi-label data sets validate the superiority of LSMM in learning effective similarity metrics for multi-label classification.

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AAAI Conference 2024 Conference Paper

Long-Tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation

  • Yuheng Jia
  • Xiaorui Peng
  • Ran Wang
  • Min-Ling Zhang

In partial label learning (PLL), each instance is associated with a set of candidate labels, among which only one is correct. The traditional PLL almost all implicitly assume that the distribution of the classes is balanced. However, in real-world applications, the distribution of the classes is imbalanced or long-tailed, leading to the long-tailed partial label learning problem. The previous methods solve this problem mainly by ameliorating the ability to learn in the tail classes, which will sacrifice the performance of the head classes. While keeping the performance of the head classes may degrade the performance of the tail classes. Therefore, in this paper, we construct two classifiers, i.e., a head classifier for keeping the performance of dominant classes and a tail classifier for improving the performance of the tail classes. Then, we propose a classifier weight estimation module to automatically estimate the shot belongingness (head class or tail class) of the samples and allocate the weights for the head classifier and tail classifier when making prediction. This cooperation improves the prediction ability for both the head classes and the tail classes. The experiments on the benchmarks demonstrate the proposed approach improves the accuracy of the SOTA methods by a substantial margin. Code and data are available at: https://github.com/pruirui/HTC-LTPLL.

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NeurIPS Conference 2024 Conference Paper

Multi-Instance Partial-Label Learning with Margin Adjustment

  • Wei Tang
  • Yin-Fang Yang
  • Zhaofei Wang
  • Weijia Zhang
  • Min-Ling Zhang

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i. e. , Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distributionloss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms.

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NeurIPS Conference 2024 Conference Paper

Multi-Label Open Set Recognition

  • Yi-Bo Wang
  • Jun-Yi Hang
  • Min-Ling Zhang

In multi-label learning, each training instance is associated with multiple labels simultaneously. Traditional multi-label learning studies primarily focus on closed set scenario, i. e. the class label set of test data is identical to those used in training phase. Nevertheless, in numerous real-world scenarios, the environment is open and dynamic where unknown labels may emerge gradually during testing. In this paper, the problem of multi-label open set recognition (MLOSR) is investigated, which poses significant challenges in classifying and recognizing instances with unknown labels in multi-label setting. To enable open set multi-label prediction, a novel approach named SLAN is proposed by leveraging sub-labeling information enriched by structural information in the feature space. Accordingly, unknown labels are recognized by differentiating the sub-labeling information from holistic supervision. Experimental results on various datasets validate the effectiveness of the proposed approach in dealing with the MLOSR problem.

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IJCAI Conference 2024 Conference Paper

Unlearning from Weakly Supervised Learning

  • Yi Tang
  • Yi Gao
  • Yong-gang Luo
  • Ju-Cheng Yang
  • Miao Xu
  • Min-Ling Zhang

Machine unlearning provides users with the right to remove their privacy data from a well-trained model. Existing approaches of machine unlearning mainly focus on exploring data removing within supervised learning (SL) tasks. However, weakly supervised learning (WSL) is more applicable to real-world scenarios since collecting WSL data is less laborious than collecting fully supervised data. In this paper, we first propose a machine unlearning approach for WSL by updating the model parameters. Motivated by the uniform distributions of untrained model predictions, we derive a formulated target to force the model's predictions of removed data to be indistinguishable. This encourages the model to forget its ability to recognize features of data slated for unlearning. Moreover, we employ formulated targets to transform the classification unlearning into the convex regression, which can significantly reduce computational cost and avoid extra information storage during the training process. Additionally, we discuss how to design a target to ensure the models' predictions of removed data being indistinguishable in different learning scenarios, e. g. , SL or WSL. As the flexibility in formulating targets, the proposed approach effectively deals with the WSL problem while still excels in SL models. Empirical studies show the superiority of the proposed approach.

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NeurIPS Conference 2024 Conference Paper

Vision-Language Models are Strong Noisy Label Detectors

  • Tong Wei
  • Hao-Tian Li
  • Chun-Shu Li
  • Jiang-Xin Shi
  • Yu-Feng Li
  • Min-Ling Zhang

Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled data in real-world applications poses a significant obstacle during the fine-tuning process. To address this challenge, this paper presents a Denoising Fine-Tuning framework, called DeFT, for adapting vision-language models. DeFT utilizes the robust alignment of textual and visual features pre-trained on millions of auxiliary image-text pairs to sieve out noisy labels. The proposed framework establishes a noisy label detector by learning positive and negative textual prompts for each class. The positive prompt seeks to reveal distinctive features of the class, while the negative prompt serves as a learnable threshold for separating clean and noisy samples. We employ parameter-efficient fine-tuning for the adaptation of a pre-trained visual encoder to promote its alignment with the learned textual prompts. As a general framework, DeFT can seamlessly fine-tune many pre-trained models to downstream tasks by utilizing carefully selected clean samples. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DeFT in both noisy label detection and image classification. Our source code can be found in the supplementary material.

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NeurIPS Conference 2024 Conference Paper

What Makes Partial-Label Learning Algorithms Effective?

  • Jiaqi Lv
  • Yangfan Liu
  • Shiyu Xia
  • Ning Xu
  • Miao Xu
  • Gang Niu
  • Min-Ling Zhang
  • Masashi Sugiyama

A partial label (PL) specifies a set of candidate labels for an instance and partial-label learning (PLL) trains multi-class classifiers with PLs. Recently, many methods that incorporate techniques from other domains have shown strong potential. The expectation that stronger techniques would enhance performance has resulted in prominent PLL methods becoming not only highly complicated but also quite different from one another, making it challenging to choose the best direction for future algorithm design. While it is exciting to see higher performance, this leaves open a fundamental question: what makes a PLL method effective? We present a comprehensive empirical analysis of this question and summarize the success of PLL so far into some minimal algorithm design principles. Our findings reveal that high accuracy on benchmark-simulated datasets with PLs can misleadingly amplify the perceived effectiveness of some general techniques, which may improve representation learning but have limited impact on addressing the inherent challenges of PLs. We further identify the common behavior among successful PLL methods as a progressive transition from uniform to one-hot pseudo-labels, highlighting the critical role of mini-batch PL purification in achieving top performance. Based on our findings, we introduce a minimal working algorithm that is surprisingly simple yet effective, and propose an improved strategy to implement the design principles, suggesting a promising direction for improvements in PLL.

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NeurIPS Conference 2023 Conference Paper

Binary Classification with Confidence Difference

  • Wei Wang
  • Lei Feng
  • Yuchen Jiang
  • Gang Niu
  • Min-Ling Zhang
  • Masashi Sugiyama

Recently, learning with soft labels has been shown to achieve better performance than learning with hard labels in terms of model generalization, calibration, and robustness. However, collecting pointwise labeling confidence for all training examples can be challenging and time-consuming in real-world scenarios. This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. Instead of pointwise labeling confidence, we are given only unlabeled data pairs with confidence difference that specifies the difference in the probabilities of being positive. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound achieves the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven. Extensive experiments on benchmark data sets and a real-world recommender system data set validate the effectiveness of our proposed approaches in exploiting the supervision information of the confidence difference.

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AAAI Conference 2023 Conference Paper

Can Label-Specific Features Help Partial-Label Learning?

  • Ruo-Jing Dong
  • Jun-Yi Hang
  • Tong Wei
  • Min-Ling Zhang

Partial label learning (PLL) aims to learn from inexact data annotations where each training example is associated with a coarse candidate label set. Due to its practicability, many PLL algorithms have been proposed in recent literature. Most prior PLL works attempt to identify the ground-truth labels from candidate sets and the classifier is trained afterward by fitting the features of examples and their exact ground-truth labels. From a different perspective, we propose to enrich the feature space and raise the question ``Can label-specific features help PLL?'' rather than learning from examples with identical features for all classes. Despite its benefits, previous label-specific feature approaches rely on ground-truth labels to split positive and negative examples of each class and then conduct clustering analysis, which is not directly applicable in PLL. To remedy this problem, we propose an uncertainty-aware confidence region to accommodate false positive labels. We first employ graph-based label enhancement to yield smooth pseudo-labels and facilitate the confidence region split. After acquiring label-specific features, a family of binary classifiers is induced. Extensive experiments on both synthesized and real-world datasets are conducted and the results show that our method consistently outperforms eight baselines. Our code is released at https://github.com/meteoseeker/UCL

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NeurIPS Conference 2023 Conference Paper

Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning

  • Wei Tang
  • Weijia Zhang
  • Min-Ling Zhang

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i. e. , Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches.

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IJCAI Conference 2023 Conference Paper

Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps

  • Jun-Xiang Mao
  • Wei Wang
  • Min-Ling Zhang

In multi-label classification, it is critical to capitalize on complicated data structures and semantic relationships. Metric learning serves as an effective strategy to provide a better measurement of distances between examples. Existing works on metric learning for multi-label classification mainly learn one single global metric that characterizes latent semantic similarity between multi-label instances. However, such single-semantics metric exploitation approaches can not capture the intrinsic properties of multi-label data possessed of rich semantics. In this paper, the first attempt towards multi-semantics metric learning for multi-label classification is investigated. Specifically, the proposed LIMIC approach simultaneously learns one global and multiple label-specific local metrics by exploiting label-specific side information. The global metric is learned to capture the commonality across all the labels and label-specific local metrics characterize the individuality of each semantic space. The combination of global metric and label-specific local metrics is utilized to construct latent semantic space for each label, in which similar intra-class instances are pushed closer and inter-class instances are pulled apart. Furthermore, metric-based label correlation regularization is constructed to maintain similarity between correlated label spaces. Extensive experiments on benchmark multi-label data sets validate the superiority of our proposed approach in learning effective distance metrics for multi-label classification.

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ICML Conference 2023 Conference Paper

Nearly-tight Bounds for Deep Kernel Learning

  • Yifan Zhang
  • Min-Ling Zhang

The generalization analysis of deep kernel learning (DKL) is a crucial and open problem of kernel methods for deep learning. The implicit nonlinear mapping in DKL makes existing methods of capacity-based generalization analysis for deep learning invalid. In an attempt to overcome this challenge and make up for the gap in the generalization theory of DKL, we develop an analysis method based on the composite relationship of function classes and derive capacity-based bounds with mild dependence on the depth, which generalizes learning theory bounds to deep kernels and serves as theoretical guarantees for the generalization of DKL. In this paper, we prove novel and nearly-tight generalization bounds based on the uniform covering number and the Rademacher chaos complexity for deep (multiple) kernel machines. In addition, for some common classes, we estimate their uniform covering numbers and Rademacher chaos complexities by bounding their pseudo-dimensions and kernel pseudo-dimensions, respectively. The mild bounds without strong assumptions partially explain the good generalization ability of deep learning combined with kernel methods.

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NeurIPS Conference 2023 Conference Paper

Partial Multi-Label Learning with Probabilistic Graphical Disambiguation

  • Jun-Yi Hang
  • Min-Ling Zhang

In partial multi-label learning (PML), each training example is associated with a set of candidate labels, among which only some labels are valid. As a common strategy to tackle PML problem, disambiguation aims to recover the ground-truth labeling information from such inaccurate annotations. However, existing approaches mainly rely on heuristics or ad-hoc rules to disambiguate candidate labels, which may not be universal enough in complicated real-world scenarios. To provide a principled way for disambiguation, we make a first attempt to explore the probabilistic graphical model for PML problem, where a directed graph is tailored to infer latent ground-truth labeling information from the generative process of partial multi-label data. Under the framework of stochastic gradient variational Bayes, a unified variational lower bound is derived for this graphical model, which is further relaxed probabilistically so that the desired prediction model can be induced with simultaneously identified ground-truth labeling information. Comprehensive experiments on multiple synthetic and real-world data sets show that our approach outperforms the state-of-the-art counterparts.

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AAAI Conference 2023 Conference Paper

Partial-Label Regression

  • Xin Cheng
  • Deng-Bao Wang
  • Lei Feng
  • Min-Ling Zhang
  • Bo An

Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial-label learning only focused on the classification setting where candidate labels are all discrete, which cannot handle continuous labels with real values. In this paper, we provide the first attempt to investigate partial-label regression, where each training example is annotated with a set of real-valued candidate labels. To solve this problem, we first propose a simple baseline method that takes the average loss incurred by candidate labels as the predictive loss. The drawback of this method lies in that the loss incurred by the true label may be overwhelmed by other false labels. To overcome this drawback, we propose an identification method that takes the least loss incurred by candidate labels as the predictive loss. We further improve it by proposing a progressive identification method to differentiate candidate labels using progressively updated weights for incurred losses. We prove that the latter two methods are model-consistent and provide convergence analysis showing the optimal parametric convergence rate. Our proposed methods are theoretically grounded and can be compatible with any models, optimizers, and losses. Experiments validate the effectiveness of our proposed methods.

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IJCAI Conference 2023 Conference Paper

Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification

  • Teng Huang
  • Bin-Bin Jia
  • Min-Ling Zhang

In multi-dimensional classification (MDC), each training example is associated with multiple class variables from different class spaces. However, it is rather costly to collect labeled MDC examples which have to be annotated from several dimensions (class spaces). To reduce the labeling cost, we attempt to deal with the MDC problem under the semi-supervised learning setting. Accordingly, a novel MDC approach named PLAP is proposed to solve the resulting semi-supervised MDC problem. Overall, PLAP works under the label propagation framework to utilize unlabeled data. To further consider dependencies among class spaces, PLAP deals with each class space in a progressive manner, where the previous propagation results will be used to initialize the current propagation procedure and all processed class spaces and the current one will be regarded as an entirety. Experiments validate the effectiveness of the proposed approach.

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IJCAI Conference 2023 Conference Paper

Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels

  • Hao-Tian Li
  • Tong Wei
  • Hao Yang
  • Kun Hu
  • Chong Peng
  • Li-Bo Sun
  • Xun-Liang Cai
  • Min-Ling Zhang

Deep neural networks have shown promising results on a wide variety of tasks using large-scale and well-annotated training datasets. However, data collected from real-world applications can suffer from two prevalent biases, i. e. , long-tailed class distribution and label noise. Previous efforts on long-tailed learning and label-noise learning can only address a single type of data bias, leading to a severe deterioration of their performance. In this paper, we propose a distance-based sample selection algorithm called Stochastic Feature Averaging (SFA), which fits a Gaussian using the exponential running average of class centroids to capture uncertainty in representation space due to label noise and data scarcity. With SFA, we detect noisy samples based on their distances to class centroids sampled from this Gaussian distribution. Based on the identified clean samples, we then propose to train an auxiliary balanced classifier to improve the generalization for the minority class and facilitate the update of Gaussian parameters. Extensive experimental results show that SFA can enhance the performance of existing methods on both simulated and real-world datasets. Further, we propose to combine SFA with the sample-selection approach, distribution-robust, and noise-robust loss functions, resulting in significant improvement in performance over the baselines. Our code is available at https: //github. com/HotanLee/SFA

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IJCAI Conference 2023 Conference Paper

Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning

  • Yi Gao
  • Miao Xu
  • Min-Ling Zhang

Multi-label learning (MLL) usually requires assigning multiple relevant labels to each instance. While a fully supervised MLL dataset needs a large amount of labeling effort, using complementary labels can help alleviate this burden. However, current approaches to learning from complementary labels are mainly designed for multi-class learning and assume that each instance has a single relevant label. This means that these approaches cannot be easily applied to MLL when only complementary labels are provided, where the number of relevant labels is unknown and can vary across instances. In this paper, we first propose the unbiased risk estimator for the multi-labeled complementary label learning (MLCLL) problem. We also provide an estimation error bound to ensure the convergence of the empirical risk estimator. In some cases, the unbiased estimator may give unbounded gradients for certain loss functions and result in overfitting. To mitigate this problem, we improve the risk estimator by minimizing a proper loss function, which has been shown to improve gradient updates. Our experimental results demonstrate the effectiveness of the proposed approach on various datasets.

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TIST Journal 2022 Journal Article

CAFE and SOUP: Toward Adaptive VDI Workload Prediction

  • Yao Zhang
  • Wenping Fan
  • Qichen Hao
  • Xinya Wu
  • Min-Ling Zhang

For Virtual Desktop Infrastructure (VDI) system, effective resource management is rather important where turning off spare virtual machines would help save running cost while maintaining sufficient virtual machines is essential to secure satisfactory user experience. Current VDI resource management strategy works in a passive manner by either reactively driving available capacity based on user demands or following manually configured schedules, which may lead to unnecessary running costs or unsatisfactory user experience. In this article, we propose a first attempt toward proactive VDI resource management, where two adaptive learning approaches for VDI workload prediction are proposed by learning from multi-grained historical features. For non-persistent desktop pool, based on the aggregation session count of pool-sharing users, the CAFE approach induces a pool-level workload predictive model by utilizing coarse-to-fine historical features extracted from aggregation workload data. For persistent desktop pool, based on the session connection status of individual users within the same pool, the SOUP approach induces user-level workload predictive model by incorporating encoded multi-grained features extracted from the logon behavior of individual users into an aggregation pool-level model. Extensive experiments on datasets of real VDI customers and electricity load evidently verify the effectiveness of the proposed adaptive approaches for VDI workload prediction as well as other workload prediction tasks.

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ICML Conference 2022 Conference Paper

Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification

  • Jun-Yi Hang
  • Min-Ling Zhang

Label-specific features serve as an effective strategy to facilitate multi-label classification, which account for the distinct discriminative properties of each class label via tailoring its own features. Existing approaches implement this strategy in a quite straightforward way, i. e. finding the most pertinent and discriminative features for each class label and directly inducing classifiers on constructed label-specific features. In this paper, we propose a dual perspective for label-specific feature learning, where label-specific discriminative properties are considered by identifying each label’s own non-informative features and making the discrimination process immutable to variations of these features. To instantiate it, we present a perturbation-based approach DELA to provide classifiers with label-specific immutability on simultaneously identified non-informative features, which is optimized towards a probabilistically-relaxed expected risk minimization problem. Comprehensive experiments on 10 benchmark data sets show that our approach outperforms the state-of-the-art counterparts.

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AAAI Conference 2022 Conference Paper

End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification

  • Jun-Yi Hang
  • Min-Ling Zhang
  • Yanghe Feng
  • Xiaocheng Song

Label-specific features serve as an effective strategy to learn from multi-label data with tailored features accounting for the distinct discriminative properties of each class label. Existing prototype-based label-specific feature transformation approaches work in a three-stage framework, where prototype acquisition, label-specific feature generation and classification model induction are performed independently. Intuitively, this separate framework is suboptimal due to its decoupling nature. In this paper, we make a first attempt towards a unified framework for prototype-based label-specific feature transformation, where the prototypes and the labelspecific features are directly optimized for classification. To instantiate it, we propose modelling the prototypes probabilistically by the normalizing flows, which possess adaptive prototypical complexity to fully capture the underlying properties of each class label and allow for scalable stochastic optimization. Then, a label correlation regularized probabilistic latent metric space is constructed via jointly learning the prototypes and the metric-based label-specific features for classification. Comprehensive experiments on 14 benchmark data sets show that our approach outperforms the state-of-the-art counterparts.

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NeurIPS Conference 2022 Conference Paper

Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization

  • Weijia Zhang
  • Xuanhui Zhang
  • Hanwen Deng
  • Min-Ling Zhang

Multi-instance learning (MIL) deals with objects represented as bags of instances and can predict instance labels from bag-level supervision. However, significant performance gaps exist between instance-level MIL algorithms and supervised learners since the instance labels are unavailable in MIL. Most existing MIL algorithms tackle the problem by treating multi-instance bags as harmful ambiguities and predicting instance labels by reducing the supervision inexactness. This work studies MIL from a new perspective by considering bags as auxiliary information, and utilize it to identify instance-level causal representations from bag-level weak supervision. We propose the CausalMIL algorithm, which not only excels at instance label prediction but also provides robustness to distribution change by synergistically integrating MIL with identifiable variational autoencoder. Our approach is based on a practical and general assumption: the prior distribution over the instance latent representations belongs to the non-factorized exponential family conditioning on the multi-instance bags. Experiments on synthetic and real-world datasets demonstrate that our approach significantly outperforms various baselines on instance label prediction and out-of-distribution generalization tasks.

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NeurIPS Conference 2022 Conference Paper

One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement

  • Ning Xu
  • Congyu Qiao
  • Jiaqi Lv
  • Xin Geng
  • Min-Ling Zhang

Multi-label learning (MLL) learns from the examples each associated with multiple labels simultaneously, where the high cost of annotating all relevant labels for each training example is challenging for real-world applications. To cope with the challenge, we investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label and show that one can successfully learn a theoretically grounded multi-label classifier for the problem. In this paper, a novel SPMLL method named SMILE, i. e. , Single-positive MultI-label learning with Label Enhancement, is proposed. Specifically, an unbiased risk estimator is derived, which could be guaranteed to approximately converge to the optimal risk minimizer of fully supervised learning and shows that one positive label of each instance is sufficient to train the predictive model. Then, the corresponding empirical risk estimator is established via recovering the latent soft label as a label enhancement process, where the posterior density of the latent soft labels is approximate to the variational Beta density parameterized by an inference model. Experiments on benchmark datasets validate the effectiveness of the proposed method.

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ICML Conference 2022 Conference Paper

Revisiting Consistency Regularization for Deep Partial Label Learning

  • Dong-Dong Wu
  • Deng-Bao Wang
  • Min-Ling Zhang

Partial label learning (PLL), which refers to the classification task where each training instance is ambiguously annotated with a set of candidate labels, has been recently studied in deep learning paradigm. Despite advances in recent deep PLL literature, existing methods (e. g. , methods based on self-training or contrastive learning) are confronted with either ineffectiveness or inefficiency. In this paper, we revisit a simple idea namely consistency regularization, which has been shown effective in traditional PLL literature, to guide the training of deep models. Towards this goal, a new regularized training framework, which performs supervised learning on non-candidate labels and employs consistency regularization on candidate labels, is proposed for PLL. We instantiate the regularization term by matching the outputs of multiple augmentations of an instance to a conformal label distribution, which can be adaptively inferred by the closed-form solution. Experiments on benchmark datasets demonstrate the superiority of the proposed method compared with other state-of-the-art methods.

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IJCAI Conference 2021 Conference Paper

BAMBOO: A Multi-instance Multi-label Approach Towards VDI User Logon Behavior Modeling

  • Wenping Fan
  • Yao Zhang
  • Qichen Hao
  • Xinya Wu
  • Min-Ling Zhang

Different to traditional on-premise VDI, the virtual desktops in DaaS (Desktop as a Service) are hosted in public cloud where virtual machines are charged based on usage. Accordingly, an adaptive power management system which can turn off spare virtual machines without sacrificing end user experience is of significant customer value as it can greatly help reduce the running cost. Generally, logon behavior modeling for VDI users serves as the key enabling-technique to fulfill intelligent power management. Prior attempts work by modeling logon behavior in a user-dependent manner with tailored single-instance feature representation, where the strong relationships among pool-sharing VDI users are ignored in the modeling framework. In this paper, a novel formulation towards VDI user logon behavior modeling is proposed by employing the multi-instance multi-label (MIML) techniques. Specifically, each user is grouped with supporting users whose behaviors are jointly modeled in the feature space with multi-instance representation as well as in the output space with multi-label prediction. The resulting MIML formulation is optimized by adapting the popular MIML boosting procedure via balanced error-rate minimization. Experimental studies on real VDI customers' data clearly validate the effectiveness of the proposed MIML-based approach against state-of-the-art VDI user logon behavior modeling techniques.

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IJCAI Conference 2021 Conference Paper

Correlation-Guided Representation for Multi-Label Text Classification

  • Qian-Wen Zhang
  • Ximing Zhang
  • Zhao Yan
  • Ruifang Liu
  • Yunbo Cao
  • Min-Ling Zhang

Multi-label text classification is an essential task in natural language processing. Existing multi-label classification models generally consider labels as categorical variables and ignore the exploitation of label semantics. In this paper, we view the task as a correlation-guided text representation problem: an attention-based two-step framework is proposed to integrate text information and label semantics by jointly learning words and labels in the same space. In this way, we aim to capture high-order label-label correlations as well as context-label correlations. Specifically, the proposed approach works by learning token-level representations of words and labels globally through a multi-layer Transformer and constructing an attention vector through word-label correlation matrix to generate the text representation. It ensures that relevant words receive higher weights than irrelevant words and thus directly optimizes the classification performance. Extensive experiments over benchmark multi-label datasets clearly validate the effectiveness of the proposed approach, and further analysis demonstrates that it is competitive in both predicting low-frequency labels and convergence speed.

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ICML Conference 2021 Conference Paper

Discriminative Complementary-Label Learning with Weighted Loss

  • Yi Gao 0003
  • Min-Ling Zhang

Complementary-label learning (CLL) deals with the weak supervision scenario where each training instance is associated with one \emph{complementary} label, which specifies the class label that the instance does \emph{not} belong to. Given the training instance ${\bm x}$, existing CLL approaches aim at modeling the \emph{generative} relationship between the complementary label $\bar y$, i. e. $P(\bar y\mid {\bm x})$, and the ground-truth label $y$, i. e. $P(y\mid {\bm x})$. Nonetheless, as the ground-truth label is not directly accessible for complementarily labeled training instance, strong generative assumptions may not hold for real-world CLL tasks. In this paper, we derive a simple and theoretically-sound \emph{discriminative} model towards $P(\bar y\mid {\bm x})$, which naturally leads to a risk estimator with estimation error bound at $\mathcal{O}(1/\sqrt{n})$ convergence rate. Accordingly, a practical CLL approach is proposed by further introducing weighted loss to the empirical risk to maximize the predictive gap between potential ground-truth label and complementary label. Extensive experiments clearly validate the effectiveness of the proposed discriminative complementary-label learning approach.

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AAAI Conference 2021 Conference Paper

Exploiting Unlabeled Data via Partial Label Assignment for Multi-Class Semi-Supervised Learning

  • Zhen-Ru Zhang
  • Qian-Wen Zhang
  • Yunbo Cao
  • Min-Ling Zhang

In semi-supervised learning, one key strategy in exploiting unlabeled data is trying to estimate its pseudo-label based on current predictive model, where the unlabeled data assigned with pseudo-label is further utilized to enlarge labeled data set for model update. Nonetheless, the supervision information conveyed by pseudo-label is prone to error especially when the performance of initial predictive model is mediocre due to limited amount of labeled data. In this paper, an intermediate unlabeled data exploitation strategy is investigated via partial label assignment, i. e. a set of candidate labels other than a single pseudo-label are assigned to the unlabeled data. We only assume that the ground-truth label of unlabeled data resides in the assigned candidate label set, which is less errorprone than trying to identify the single ground-truth label via pseudo-labeling. Specifically, a multi-class classifier is induced from the partial label examples with candidate labels to facilitate model induction with labeled examples. An iterative procedure is designed to enable labeling information communication between the classifiers induced from partial label examples and labeled examples, whose classification outputs are integrated to yield the final prediction. Comparative studies against state-of-the-art approaches clearly show the effectiveness of the proposed unlabeled data exploitation strategy for multi-class semi-supervised learning.

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NeurIPS Conference 2021 Conference Paper

Instance-Dependent Partial Label Learning

  • Ning Xu
  • Congyu Qiao
  • Xin Geng
  • Min-Ling Zhang

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. However, this assumption is not realistic since the candidate labels are always instance-dependent. In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature. The incorrect label with a high degree is more likely to be annotated as the candidate label. Therefore, the latent label distribution is the essential labeling information in partially labeled examples and worth being leveraged for predictive model training. Motivated by this consideration, we propose a novel PLL method that recovers the label distribution as a label enhancement (LE) process and trains the predictive model iteratively in every epoch. Specifically, we assume the true posterior density of the latent label distribution takes on the variational approximate Dirichlet density parameterized by an inference model. Then the evidence lower bound is deduced for optimizing the inference model and the label distributions generated from the variational posterior are utilized for training the predictive model. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method. Source code is available at https: //github. com/palm-ml/valen.

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IJCAI Conference 2021 Conference Paper

Learning from Complementary Labels via Partial-Output Consistency Regularization

  • Deng-Bao Wang
  • Lei Feng
  • Min-Ling Zhang

In complementary-label learning (CLL), a multi-class classifier is learned from training instances each associated with complementary labels, which specify the classes that the instance does not belong to. Previous studies focus on unbiased risk estimator or surrogate loss while neglect the importance of regularization in training phase. In this paper, we give the first attempt to leverage regularization techniques for CLL. By decoupling a label vector into complementary labels and partial unknown labels, we simultaneously inhibit the outputs of complementary labels with a complementary loss and penalize the sensitivity of the classifier on the partial outputs of these unknown classes by consistency regularization. Then we unify the complementary loss and consistency loss together by a specially designed dynamic weighting factor. We conduct a series of experiments showing that the proposed method achieves highly competitive performance in CLL.

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AAAI Conference 2021 Conference Paper

Learning from Noisy Labels with Complementary Loss Functions

  • Deng-Bao Wang
  • Yong Wen
  • Lujia Pan
  • Min-Ling Zhang

Recent researches reveal that deep neural networks are sensitive to label noises hence leading to poor generalization performance in some tasks. Although different robust loss functions have been proposed to remedy this issue, they suffer from an underfitting problem, thus are not sufficient to learn accurate models. On the other hand, the commonly used Cross Entropy (CE) loss, which shows high performance in standard supervised learning (with clean supervision), is nonrobust to label noise. In this paper, we propose a general framework to learn robust deep neural networks with complementary loss functions. In our framework, CE and robust loss play complementary roles in a joint learning objective as per their learning sufficiency and robustness properties respectively. Specifically, we find that by exploiting the memorization effect of neural networks, we can easily filter out a proportion of hard samples and generate reliable pseudo labels for easy samples, and thus reduce the label noise to a quite low level. Then, we simply learn with CE on pseudo supervision and robust loss on original noisy supervision. In this procedure, CE can guarantee the sufficiency of optimization while the robust loss can be regarded as the supplement. Experimental results on benchmark classification datasets indicate that the proposed method helps achieve robust and sufficient deep neural network training simultaneously.

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ICML Conference 2021 Conference Paper

Multi-Dimensional Classification via Sparse Label Encoding

  • Bin-Bin Jia 0001
  • Min-Ling Zhang

In multi-dimensional classification (MDC), there are multiple class variables in the output space with each of them corresponding to one heterogeneous class space. Due to the heterogeneity of class spaces, it is quite challenging to consider the dependencies among class variables when learning from MDC examples. In this paper, we propose a novel MDC approach named SLEM which learns the predictive model in an encoded label space instead of the original heterogeneous one. Specifically, SLEM works in an encoding-training-decoding framework. In the encoding phase, each class vector is mapped into a real-valued one via three cascaded operations including pairwise grouping, one-hot conversion and sparse linear encoding. In the training phase, a multi-output regression model is learned within the encoded label space. In the decoding phase, the predicted class vector is obtained by adapting orthogonal matching pursuit over outputs of the learned multi-output regression model. Experimental results clearly validate the superiority of SLEM against state-of-the-art MDC approaches.

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NeurIPS Conference 2021 Conference Paper

Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence

  • Deng-Bao Wang
  • Lei Feng
  • Min-Ling Zhang

Capturing accurate uncertainty quantification of the prediction from deep neural networks is important in many real-world decision-making applications. A reliable predictor is expected to be accurate when it is confident about its predictions and indicate high uncertainty when it is likely to be inaccurate. However, modern neural networks have been found to be poorly calibrated, primarily in the direction of overconfidence. In recent years, there is a surge of research on model calibration by leveraging implicit or explicit regularization techniques during training, which obtain well calibration by avoiding overconfident outputs. In our study, we empirically found that despite the predictions obtained from these regularized models are better calibrated, they suffer from not being as calibratable, namely, it is harder to further calibrate their predictions with post-hoc calibration methods like temperature scaling and histogram binning. We conduct a series of empirical studies showing that overconfidence may not hurt final calibration performance if post-hoc calibration is allowed, rather, the penalty of confident outputs will compress the room of potential improvements in post-hoc calibration phase. Our experimental findings point out a new direction to improve calibration of DNNs by considering main training and post-hoc calibration as a unified framework.

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AAAI Conference 2020 Conference Paper

Maximum Margin Multi-Dimensional Classification

  • Bin-Bin Jia
  • Min-Ling Zhang

Multi-dimensional classification (MDC) assumes heterogenous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. Due to the heterogeneity of class spaces, the major difficulty in designing marginbased MDC techniques lies in that the modeling outputs from different class spaces are not comparable to each other. In this paper, a first attempt towards maximum margin multidimensional classification is investigated. Following the onevs-one decomposition within each class space, the resulting models are optimized by leveraging classification margin maximization on individual class variable and model relationship regularization across class variables. We derive convex formulation for the maximum margin MDC problem, which can be tackled with alternating optimization admitting QP or closed-form solution in either alternating step. Experimental studies over real-world MDC data sets clearly validate effectiveness of the proposed maximum margin MDC techniques.

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AAAI Conference 2020 Conference Paper

Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

  • Ze-Sen Chen
  • Xuan Wu
  • Qing-Guo Chen
  • Yao Hu
  • Min-Ling Zhang

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.

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NeurIPS Conference 2020 Conference Paper

Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization

  • Wei Wang
  • Min-Ling Zhang

Partial label learning assumes inaccurate supervision where each training example is associated with a set of candidate labels, among which only one is valid. In many real-world scenarios, however, it is costly and time-consuming to assign candidate label sets to all the training examples. To circumvent this difficulty, the problem of semi-supervised partial label learning is investigated in this paper, where unlabeled data is utilized to facilitate model induction along with partial label training examples. Specifically, label propagation is adopted to instantiate the labeling confidence of partial label examples. After that, maximum margin formulation is introduced to jointly enable the induction of predictive model and the estimation of labeling confidence over unlabeled data. The derived formulation enforces confidence-rated margin maximization and confidence manifold preservation over partial label examples and unlabeled data. We show that the predictive model and labeling confidence can be solved via alternating optimization which admits QP solutions in either alternating step. Extensive experiments on synthetic as well as real-world data sets clearly validate the effectiveness of the proposed semi-supervised partial label learning approach.

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AAAI Conference 2019 Conference Paper

CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features

  • Yao Zhang
  • Wen-Ping Fan
  • Xuan Wu
  • Hua Chen
  • Bin-Yang Li
  • Min-Ling Zhang

Virtual desktop infrastructure (VDI) is a virtualization technology that hosts desktop operating system on centralized server in a data center of private or public cloud. Effective resource management is of crucial importance for VDI customers, where maintaining sufficient virtual machines helps guarantee satisfactory user experience while turning off spare virtual machines helps save running cost. Generally, existing techniques work in passive manner by either driving available capacity reactively or configuring management schedules manually. In this paper, a novel proactive resource management approach is proposed which aims to predict VDI pool workload adaptively by utilizing CoArse to Fine historical dEscriptive (CAFE) features. Specifically, aggregate session count from pool end users serves as the basis for workload measurement and predictive model induction. Extensive experiments on real VDI customers data sets clearly validate the effectiveness of multi-grained features for VDI workload prediction. Furthermore, practical insights identified in our VDI data analytics are also discussed.

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AAAI Conference 2019 Conference Paper

Multi-Dimensional Classification via kNN Feature Augmentation

  • Bin-Bin Jia
  • Min-Ling Zhang

Multi-dimensional classification (MDC) deals with the problem where one instance is associated with multiple class variables, each of which specifies its class membership w. r. t. one specific class space. Existing approaches learn from MDC examples by focusing on modeling dependencies among class variables, while the potential usefulness of manipulating feature space hasn’t been investigated. In this paper, a first attempt towards feature manipulation for MDC is proposed which enriches the original feature space with kNNaugmented features. Specifically, simple counting statistics on the class membership of neighboring MDC examples are used to generate augmented feature vector. In this way, discriminative information from class space is encoded into the feature space to help train the multi-dimensional classification model. To validate the effectiveness of the proposed feature augmentation techniques, extensive experiments over eleven benchmark data sets as well as four state-of-the-art MDC approaches are conducted. Experimental results clearly show that, compared to the original feature space, classification performance of existing MDC approaches can be significantly improved by incorporating kNN-augmented features.

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IJCAI Conference 2019 Conference Paper

Multi-View Multi-Label Learning with View-Specific Information Extraction

  • Xuan Wu
  • Qing-Guo Chen
  • Yao Hu
  • Dengbao Wang
  • Xiaodong Chang
  • Xiaobo Wang
  • Min-Ling Zhang

Multi-view multi-label learning serves an important framework to learn from objects with diverse representations and rich semantics. Existing multi-view multi-label learning techniques focus on exploiting shared subspace for fusing multi-view representations, where helpful view-specific information for discriminative modeling is usually ignored. In this paper, a novel multi-view multi-label learning approach named SIMM is proposed which leverages shared subspace exploitation and view-specific information extraction. For shared subspace exploitation, SIMM jointly minimizes confusion adversarial loss and multi-label loss to utilize shared information from all views. For view-specific information extraction, SIMM enforces an orthogonal constraint w. r. t. the shared subspace to utilize view-specific discriminative information. Extensive experiments on real-world data sets clearly show the favorable performance of SIMM against other state-of-the-art multi-view multi-label learning approaches.

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AAAI Conference 2019 Conference Paper

Partial Multi-Label Learning via Credible Label Elicitation

  • Jun-Peng Fang
  • Min-Ling Zhang

In partial multi-label learning (PML), each training example is associated with multiple candidate labels which are only partially valid. The task of PML naturally arises in learning scenarios with inaccurate supervision, and the goal is to induce a multi-label predictor which can assign a set of proper labels for unseen instance. To learn from PML training examples, the training procedure is prone to be misled by the false positive labels concealed in candidate label set. In light of this major difficulty, a novel two-stage PML approach is proposed which works by eliciting credible labels from the candidate label set for model induction. In this way, most false positive labels are expected to be excluded from the training procedure. Specifically, in the first stage, the labeling confidence of candidate label for each PML training example is estimated via iterative label propagation. In the second stage, by utilizing credible labels with high labeling confidence, multi-label predictor is induced via pairwise label ranking with virtual label splitting or maximum a posteriori (MAP) reasoning. Extensive experiments on synthetic as well as real-world data sets clearly validate the effectiveness of credible label elicitation in learning from PML examples.

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AAAI Conference 2018 Conference Paper

Feature-Induced Labeling Information Enrichment for Multi-Label Learning

  • Qian-Wen Zhang
  • Yun Zhong
  • Min-Ling Zhang

In multi-label learning, each training example is represented by a single instance (feature vector) while associated with multiple class labels simultaneously. The task is to learn a predictive model from the training examples which can assign a set of proper labels for the unseen instance. Most existing approaches make use of multi-label training examples by exploiting their labeling information in a crisp manner, i. e. one class label is either fully relevant or irrelevant to the instance. In this paper, a novel multi-label learning approach is proposed which aims to enrich the labeling information by leveraging the structural information in feature space. Firstly, the underlying structure of feature space is characterized by conducting sparse reconstruction among the training examples. Secondly, the reconstruction information is conveyed from feature space to label space so as to enrich the original categorical labels into numerical ones. Thirdly, the multilabel predictive model is induced by learning from training examples with enriched labeling information. Extensive experiments on fifteen benchmark data sets clearly validate the effectiveness of the proposed feature-induced strategy for enhancing labeling information of multi-label examples.

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IJCAI Conference 2018 Conference Paper

Towards Enabling Binary Decomposition for Partial Label Learning

  • Xuan Wu
  • Min-Ling Zhang

The task of partial label (PL) learning is to learn a multi-class classifier from training examples each associated with a set of candidate labels, among which only one corresponds to the ground-truth label. It is well known that for inducing multi-class predictive model, the most straightforward solution is binary decomposition which works by either one-vs-rest or one-vs-one strategy. Nonetheless, the ground-truth label for each PL training example is concealed in its candidate label set and thus not accessible to the learning algorithm, binary decomposition cannot be directly applied under partial label learning scenario. In this paper, a novel approach is proposed to solving partial label learning problem by adapting the popular one-vs-one decomposition strategy. Specifically, one binary classifier is derived for each pair of class labels, where PL training examples with distinct relevancy to the label pair are used to generate the corresponding binary training set. After that, one binary classifier is further derived for each class label by stacking over predictions of existing binary classifiers to improve generalization. Experimental studies on both artificial and real-world PL data sets clearly validate the effectiveness of the proposed binary decomposition approach w. r. t state-of-the-art partial label learning techniques.

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IJCAI Conference 2017 Conference Paper

Binary Linear Compression for Multi-label Classification

  • Wen-Ji Zhou
  • Yang Yu
  • Min-Ling Zhang

In multi-label classification tasks, labels are commonly related with each other. It has been well recognized that utilizing label relationship is essential to multi-label learning. One way to utilizing label relationship is to map labels to a lower-dimensional space of uncorrelated labels, where the relationship could be encoded in the mapping. Previous linear mapping methods commonly result in regression subproblems in the lower-dimensional label space. In this paper, we disclose that mappings to a low-dimensional multi-label regression problem can be worse than mapping to a classification problem, since regression requires more complex model than classification. We then propose the binary linear compression (BILC) method that results in a binary label space, leading to classification subproblems. Experiments on several multi-label datasets show that, employing classification in the embedded space results in much simpler models than regression, leading to smaller structure risk. The proposed methods are also shown to be superior to some state-of-the-art approaches.

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AAAI Conference 2017 Conference Paper

Confidence-Rated Discriminative Partial Label Learning

  • Cai-Zhi Tang
  • Min-Ling Zhang

Partial label learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one label is valid. The common discriminative solution to learn from partial label examples assumes one parametric model for each class label, whose predictions are aggregated to optimize specific objectives such as likelihood or margin over the training examples. Nonetheless, existing discriminative approaches treat the predictions from all parametric models in an equal manner, where the confidence of each candidate label being the ground-truth label is not differentiated. In this paper, a boosting-style partial label learning approach is proposed to enabling confidence-rated discriminative modeling. Specifically, the ground-truth confidence of each candidate label is maintained in each boosting round and utilized to train the base classifier. Extensive experiments on artificial as well as real-world partial label data sets validate the effectiveness of the confidence-rated discriminative modeling.

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AAAI Conference 2016 Conference Paper

Multi-Label Manifold Learning

  • Peng Hou
  • Xin Geng
  • Min-Ling Zhang

This paper gives an attempt to explore the manifold in the label space for multi-label learning. Traditional label space is logical, where no manifold exists. In order to study the label manifold, the label space should be extended to a Euclidean space. However, the label manifold is not explicitly available from the training examples. Fortunately, according to the smoothness assumption that the points close to each other are more likely to share a label, the local topological structure can be shared between the feature manifold and the label manifold. Based on this, we propose a novel method called ML2, i. e. , Multi-Label Manifold Learning, to reconstruct and exploit the label manifold. To our best knowledge, it is one of the first attempts to explore the manifold in the label space in multi-label learning. Extensive experiments show that the performance of multi-label learning can be improved significantly with the label manifold.

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IJCAI Conference 2015 Conference Paper

Solving the Partial Label Learning Problem: An Instance-Based Approach

  • Min-Ling Zhang
  • Fei Yu

In partial label learning, each training example is associated with a set of candidate labels, among which only one is valid. An intuitive strategy to learn from partial label examples is to treat all candidate labels equally and make prediction by averaging their modeling outputs. Nonetheless, this strategy may suffer from the problem that the modeling output from the valid label is overwhelmed by those from the false positive labels. In this paper, an instance-based approach named IPAL is proposed by directly disambiguating the candidate label set. Briefly, IPAL tries to identify the valid label of each partial label example via an iterative label propagation procedure, and then classifies the unseen instance based on minimum error reconstruction from its nearest neighbors. Extensive experiments show that IPAL compares favorably against the existing instance-based as well as other stateof-the-art partial label learning approaches.

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IJCAI Conference 2015 Conference Paper

Towards Class-Imbalance Aware Multi-Label Learning

  • Min-Ling Zhang
  • Yu-Kun Li
  • Xu-Ying Liu

In multi-label learning, each object is represented by a single instance while associated with a set of class labels. Due to the huge (exponential) number of possible label sets for prediction, existing approaches mainly focus on how to exploit label correlations to facilitate the learning process. Nevertheless, an intrinsic characteristic of learning from multi-label data, i. e. the widely-existing classimbalance among labels, has not been well investigated. Generally, the number of positive training instances w. r. t. each class label is far less than its negative counterparts, which may lead to performance degradation for most multi-label learning techniques. In this paper, a new multi-label learning approach named Cross-Coupling Aggregation (COCOA) is proposed, which aims at leveraging the exploitation of label correlations as well as the exploration of class-imbalance. Briefly, to induce the predictive model on each class label, one binary-class imbalance learner corresponding to the current label and several multi-class imbalance learners coupling with other labels are aggregated for prediction. Extensive experiments clearly validate the effectiveness of the proposed approach, especially in terms of imbalance-specific evaluation metrics such as F-measure and area under the ROC curve.

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AIJ Journal 2012 Journal Article

Multi-instance multi-label learning

  • Zhi-Hua Zhou
  • Min-Ling Zhang
  • Sheng-Jun Huang
  • Yu-Feng Li

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly.

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IJCAI Conference 2011 Conference Paper

LIFT: Multi-Label Learning with Label-Specific Features

  • Min-Ling Zhang

Multi-label learning deals with the problem where each training example is represented by a single instance while associated with a set of class labels. For an unseen example, existing approaches choose to determine the membership of each possible class label to it based on identical feature set, i. e. the very instance representation of the unseen example is employed in the discrimination processes of all labels. However, this commonly-used strategy might be suboptimal as different class labels usually carry specific characteristics of their own, and it could be beneficial to exploit different feature sets for the discrimination of different labels. Based on the above reflection, we propose a new strategy to multi-label learning by leveraging label-specific features, where a simple yet effective algorithm named LIFT is presented. Briefly, LIFT constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Extensive experiments across sixteen diversified data sets clearly validate the superiority of LIFT against other well-established multi-label learning algorithms.

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AAAI Conference 2007 Conference Paper

Multi-Label Learning by Instance Differentiation

  • Min-Ling Zhang

Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is explicitly expressed in the output space by being associated with multiple class labels. While on the other hand, its ambiguity is only implicitly encoded in the input space by being represented by only a single instance. Based on this recognition, we hypothesize that if the inherent ambiguity can be explicitly expressed in the input space appropriately, the problem of multi-label learning can be solved more effectively. We justify this hypothesis by proposing a novel multi-label learning approach named INS- DIF. The core of INSDIF is instance differentiation that transforms an example into a bag of instances each of which reflects the example’s relationship with one of the possible classes. In this way, INSDIF directly addresses the inherent ambiguity of each example in the input space. A two-level classification strategy is employed to learn from the transformed examples. Applications to automatic web page categorization, natural scene classification and gene functional analysis show that our approach outperforms several well-established multi-label learning algorithms.

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NeurIPS Conference 2006 Conference Paper

Multi-Instance Multi-Label Learning with Application to Scene Classification

  • Zhi-Li Zhang
  • Min-Ling Zhang

In this paper, we formalize multi-instance multi-label learning, where each train- ing example is associated with not only multiple instances but also multiple class labels. Such a problem can occur in many real-world tasks, e. g. an image usually contains multiple patches each of which can be described by a feature vector, and the image can belong to multiple categories since its semantics can be recognized in different ways. We analyze the relationship between multi-instance multi-label learning and the learning frameworks of traditional supervised learning, multi- instance learning and multi-label learning. Then, we propose the MIMLBOOST and MIMLSVM algorithms which achieve good performance in an application to scene classification.

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