Zenglin Xu

AAAI Conference 2026 Conference Paper

Renormalization Group Guided Tensor Network Structure Search

• Maolin Wang
• Bowen Yu
• Sheng Zhang
• Linjie Mi
• Wanyu Wang
• Yiqi Wang
• Pengyue Jia
• Xuetao Wei

Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional data representation. Despite recent advances, existing TN-SS methods face significant limitations in computational tractability, structure adaptivity, and optimization robustness across diverse tensor characteristics. They struggle with three key challenges: single-scale optimization missing multi-scale structures, discrete search spaces hindering smooth structure evolution, and separated structure-parameter optimization causing computational inefficiency. We propose RGTN (Renormalization Group guided Tensor Network search), a physics-inspired framework transforming TN-SS via multi-scale renormalization group flows. Unlike fixed-scale discrete search methods, RGTN uses dynamic scale-transformation for continuous structure evolution across resolutions. Its core innovation includes learnable edge gates for optimization-stage topology modification and intelligent proposals based on physical quantities like node tension measuring local stress, and edge information flow quantifying connectivity importance. Starting from low-complexity coarse scales and refining to finer ones, RGTN finds compact structures while escaping local minima via scale-induced perturbations. Extensive experiments on light field data, high-order synthetic tensors, and video completion tasks show RGTN achieves state-of-the-art compression ratios and runs 4-600 times faster than existing methods, validating the effectiveness of our physics-inspired approach.

ICML Conference 2025 Conference Paper

Efficient Network Automatic Relevance Determination

• Hongwei Zhang
• Ziqi Ye
• Xinyuan Wang
• Xin Guo 0010
• Zenglin Xu
• Yuan Cheng
• Zixin Hu
• Yuan (Alan) Qi

We propose Network Automatic Relevance Determination (NARD), an extension of ARD for linearly probabilistic models, to simultaneously model sparse relationships between inputs $X \in \mathbb R^{d \times N}$ and outputs $Y \in \mathbb R^{m \times N}$, while capturing the correlation structure among the $Y$. NARD employs a matrix normal prior which contains a sparsity-inducing parameter to identify and discard irrelevant features, thereby promoting sparsity in the model. Algorithmically, it iteratively updates both the precision matrix and the relationship between $Y$ and the refined inputs. To mitigate the computational inefficiencies of the $\mathcal O(m^3 + d^3)$ cost per iteration, we introduce Sequential NARD, which evaluates features sequentially, and a Surrogate Function Method, leveraging an efficient approximation of the marginal likelihood and simplifying the calculation of determinant and inverse of an intermediate matrix. Combining the Sequential update with the Surrogate Function method further reduces computational costs. The computational complexity per iteration for these three methods is reduced to $\mathcal O(m^3+p^3)$, $\mathcal O(m^3 + d^2)$, $\mathcal O(m^3+p^2)$ respectively, where $p \ll d$ is the final number of features in the model. Our methods demonstrate significant improvements in computational efficiency with comparable performance on both synthetic and real-world datasets.

TMLR Journal 2025 Journal Article

Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance

• Dun Zeng
• Zenglin Xu
• Yu Pan
• Xu Luo
• Qifan Wang
• Xiaoying Tang

Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. In FL, a server typically selects a subset of clients for each training round to optimize resource usage. Central to this process is the technique of unbiased client sampling, which ensures a representative selection of clients. Current methods primarily utilize a random sampling procedure which, despite its effectiveness, achieves suboptimal efficiency owing to the loose upper bound caused by the sampling variance. In this work, by adopting an independent sampling procedure, we propose a federated optimization framework focused on adaptive unbiased client sampling, improving the convergence rate via an online variance reduction strategy. In particular, we present the first adaptive client sampler, K-Vib, employing an independent sampling procedure. K-Vib achieves a linear speed-up on the regret bound $\tilde{\mathcal{O}}\big(N^{\frac{1}{3}}T^{\frac{2}{3}}/K^{\frac{4}{3}}\big)$ within a set communication budget $K$. Empirical studies indicate that K-Vib doubles the speed compared to baseline algorithms, demonstrating significant potential in federated optimization.

AAAI Conference 2025 Conference Paper

GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models Through Statistically-Guided Geo-Prototyping

• Bang An
• Xun Zhou
• Zirui Zhou
• Ronilo Ragodos
• Zenglin Xu
• Jun Luo

The problem of forecasting spatiotemporal events such as crimes and accidents is crucial to public safety and city management. Besides accuracy, interpretability is also a key requirement for spatiotemporal forecasting models to justify the decisions. Merely presenting predicted scores fails to convince the public and does not contribute to future urban planning. Interpretation of the spatiotemporal forecasting mechanism is, however, challenging due to the complexity of multi-source spatiotemporal features, the non-intuitive nature of spatiotemporal patterns for non-expert users, and the presence of spatial heterogeneity in the data. Currently, no existing deep learning model intrinsically interprets the complex predictive process learned from multi-source spatiotemporal features. To bridge the gap, we propose GeoPro-Net, an intrinsically interpretable spatiotemporal model for spatiotemporal event forecasting problems. GeoPro-Net introduces a novel Geo-concept convolution operation, which employs statistical tests to extract predictive patterns in the input as "Geo-concepts'', and condenses the "Geo-concept-encoded'' input through interpretable channel fusion and geographic-based pooling. In addition, GeoPro-Net learns different sets of prototypes of concepts inherently, and projects them to real-world cases for interpretation. Comprehensive experiments and case studies on four real-world datasets demonstrate that GeoPro-Net provides better interpretability while still achieving competitive prediction performance compared with state-of-the-art baselines.

ICLR Conference 2025 Conference Paper

IDInit: A Universal and Stable Initialization Method for Neural Network Training

• Yu Pan 0005
• Chaozheng Wang
• Zekai Wu
• Qifan Wang 0001
• Min Zhang 0014
• Zenglin Xu

Deep neural networks have achieved remarkable accomplishments in practice. The success of these networks hinges on effective initialization methods, which are vital for ensuring stable and rapid convergence during training. Recently, initialization methods that maintain identity transition within layers have shown good efficiency in network training. These techniques (e.g., Fixup) set specific weights to zero to achieve identity control. However, settings of remaining weight (e.g., Fixup uses random values to initialize non-zero weights) will affect the inductive bias that is achieved only by a zero weight, which may be harmful to training. Addressing this concern, we introduce fully identical initialization (IDInit), a novel method that preserves identity in both the main and sub-stem layers of residual networks. IDInit employs a padded identity-like matrix to overcome rank constraints in non-square weight matrices. Furthermore, we show the convergence problem of an identity matrix can be solved by stochastic gradient descent. Additionally, we enhance the universality of IDInit by processing higher-order weights and addressing dead neuron problems. IDInit is a straightforward yet effective initialization method, with improved convergence, stability, and performance across various settings, including large-scale datasets and deep models.

NeurIPS Conference 2025 Conference Paper

InfMasking: Unleashing Synergistic Information by Contrastive Multimodal Interactions

• Liangjian Wen
• Qun Dai
• Jianzhuang Liu
• Jiangtao Zheng
• Yong Dai
• Dongkai Wang
• Zhao Kang
• Jun Wang

In multimodal representation learning, synergistic interactions between modalities not only provide complementary information but also create unique outcomes through specific interaction patterns that no single modality could achieve alone. Existing methods may struggle to effectively capture the full spectrum of synergistic information, leading to suboptimal performance in tasks where such interactions are critical. This is particularly problematic because synergistic information constitutes the fundamental value proposition of multimodal representation. To address this challenge, we introduce InfMasking, a contrastive synergistic information extraction method designed to enhance synergistic information through an Infinite Masking strategy. InfMasking stochastically occludes most features from each modality during fusion, preserving only partial information to create representations with varied synergistic patterns. Unmasked fused representations are then aligned with masked ones through mutual information maximization to encode comprehensive synergistic information. This infinite masking strategy enables capturing richer interactions by exposing the model to diverse partial modality combinations during training. As computing mutual information estimates with infinite masking is computationally prohibitive, we derive an InfMasking loss to approximate this calculation. Through controlled experiments, we demonstrate that InfMasking effectively enhances synergistic information between modalities. In evaluations on large-scale real-world datasets, InfMasking achieves state-of-the-art performance across seven benchmarks. Code is released at https: //github. com/brightest66/InfMasking.

ICML Conference 2025 Conference Paper

Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification

• Langzhang Liang
• Fanchen Bu
• Zixing Song
• Zenglin Xu
• Shirui Pan
• Kijung Shin

The message-passing paradigm of Graph Neural Networks often struggles with exchanging information across distant nodes typically due to structural bottlenecks in certain graph regions, a limitation known as over-squashing. To reduce such bottlenecks, graph rewiring, which modifies graph topology, has been widely used. However, existing graph rewiring techniques often overlook the need to preserve critical properties of the original graph, e. g. , spectral properties. Moreover, many approaches rely on increasing edge count to improve connectivity, which introduces significant computational overhead and exacerbates the risk of over-smoothing. In this paper, we propose a novel graph-rewiring method that leverages spectral graph sparsification for mitigating over-squashing. Specifically, our method generates graphs with enhanced connectivity while maintaining sparsity and largely preserving the original graph spectrum, effectively balancing structural bottleneck reduction and graph property preservation. Experimental results validate the effectiveness of our approach, demonstrating its superiority over strong baseline methods in classification accuracy and retention of the Laplacian spectrum.

NeurIPS Conference 2025 Conference Paper

RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching

• Wenzhe Ouyang
• Qi Ye
• Jinghua Wang
• Zenglin Xu
• Jiming Chen

We introduce RFMPose, a novel generative framework for category-level 6D object pose estimation that learns deterministic pose trajectories through Riemannian Flow Matching (RFM). Existing discriminative approaches struggle with multi-hypothesis predictions (e. g. , symmetry ambiguities) and often require specialized network architectures. RFMPose advances this paradigm through three key innovations: (1) Ensuring geometric consistency via geodesic interpolation on Riemannian manifolds combined with bi-invariant metric constraints; (2) Alleviating symmetry-induced ambiguities through Riemannian Optimal Transport for probability mass redistribution without ad-hoc design; (3) Enabling end-to-end likelihood estimation through Hutchinson trace approximation, thereby eliminating auxiliary model dependencies. Extensive experiments on the Omni6DPose demonstrate state-of-the-art performance of the proposed method, with significant improvements of $\textbf{+4. 1}$ in $\mathrm{\textbf{IoU}_{25}}$ and $\textbf{+2. 4}$ in $\textbf{5°2cm}$ metrics compared to prior generative approaches. Furthermore, the proposed RFM framework exhibits robust sim-to-real transfer capabilities and facilitates pose tracking extensions with minimal architectural adaptation.

TIST Journal 2024 Journal Article

A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges

• Yifei Zhang
• Dun Zeng
• Jinglong Luo
• Xinyu Fu
• Guanzhong Chen
• Zenglin Xu
• Irwin King

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) emerges as a promising solution to safeguard personal information in distributed settings across a multitude of practical contexts. However, the realm of FL is not without its challenges. Especially worrisome are adversarial attacks targeting its algorithmic robustness and systemic confidentiality. Moreover, the presence of biases and opacity in prediction outcomes further complicates FL’s broader adoption. Consequently, there is a growing expectation for FL to instill trust. To address this, we chart out a comprehensive road-map for Trustworthy Federated Learning (TFL) and provide an overview of existing efforts across four pivotal dimensions: Privacy and Security, Robustness, Fairness, and Explainability. For each dimension, we identify potential pitfalls that might undermine TFL and present a curated selection of defensive strategies, enriched by a discourse on technical solutions tailored for TFL. Furthermore, we present potential challenges and future directions to be explored for in-depth TFL research with broader impacts.

IJCAI Conference 2024 Conference Paper

A Systematic Survey on Federated Semi-supervised Learning

• Zixing Song
• Xiangli Yang
• Yifei Zhang
• Xinyu Fu
• Zenglin Xu
• Irwin King

Federated learning (FL) revolutionizes distributed machine learning by enabling devices to collaboratively learn a model while maintaining data privacy. However, FL usually faces a critical challenge with limited labeled data, making semi-supervised learning (SSL) crucial for utilizing abundant unlabeled data. The integration of SSL within the federated framework gives rise to federated semi-supervised learning (FSSL), a novel approach that exploits unlabeled data across devices without compromising privacy. This paper systematically explores FSSL, shedding light on its four basic problem settings that commonly appear in real-world scenarios. By examining the unique challenges, generic solutions, and representative methods tailored for each setting of FSSL, we aim to provide a cohesive overview of the current state of the art and pave the way for future research directions in this promising field.

AAAI Conference 2024 Conference Paper

Discriminative Forests Improve Generative Diversity for Generative Adversarial Networks

• Junjie Chen
• Jiahao Li
• Chen Song
• Bin Li
• Qingcai Chen
• Hongchang Gao
• Wendy Hui Wang
• Zenglin Xu

Improving the diversity of Artificial Intelligence Generated Content (AIGC) is one of the fundamental problems in the theory of generative models such as generative adversarial networks (GANs). Previous studies have demonstrated that the discriminator in GANs should have high capacity and robustness to achieve the diversity of generated data. However, a discriminator with high capacity tends to overfit and guide the generator toward collapsed equilibrium. In this study, we propose a novel discriminative forest GAN, named Forest-GAN, that replaces the discriminator to improve the capacity and robustness for modeling statistics in real-world data distribution. A discriminative forest is composed of multiple independent discriminators built on bootstrapped data. We prove that a discriminative forest has a generalization error bound, which is determined by the strength of individual discriminators and the correlations among them. Hence, a discriminative forest can provide very large capacity without any risk of overfitting, which subsequently improves the generative diversity. With the discriminative forest framework, we significantly improved the performance of AutoGAN with a new record FID of 19.27 from 30.71 on STL10 and improved the performance of StyleGAN2-ADA with a new record FID of 6.87 from 9.22 on LSUN-cat.

RLJ Journal 2024 Journal Article

Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

• Bin Hu
• Chenyang Zhao
• Pu Zhang
• Zihao Zhou
• Yuanhang Yang
• Zenglin Xu
• Bin Liu

Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. Recent studies have demonstrated that LLMs can assist an embodied agent in solving complex sequential decision making tasks by providing high-level instructions. However, interactions with LLMs can be time-consuming. In many practical scenarios, it requires a significant amount of storage space that can only be deployed on remote cloud servers. Additionally, using commercial LLMs can be costly since they may charge based on usage frequency. In this paper, we explore how to enable intelligent cost-effective interactions between a down stream task oriented agent and an LLM. We find that this problem can be naturally formulated by a Markov decision process (MDP), and propose When2Ask, a reinforcement learning based approach that learns when it is necessary to query LLMs for high-level instructions to accomplish a target task. On one side, When2Ask discourages unnecessary redundant interactions, while on the other side, it enables the agent to identify and follow useful instructions from the LLM. This enables the agent to halt an ongoing plan and transition to a more suitable one based on new environmental observations. Experiments on MiniGrid and Habitat environments that entail planning sub-goals demonstrate that When2Ask learns to solve target tasks with only a few necessary interactions with the LLM, significantly reducing interaction costs in testing environments compared with baseline methods. Our code is available at: https://github.com/ZJLAB-AMMI/LLM4RL.

RLC Conference 2024 Conference Paper

Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

• Bin Hu
• Chenyang Zhao
• Pu Zhang
• Zihao Zhou
• Yuanhang Yang
• Zenglin Xu
• Bin Liu

Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. Recent studies have demonstrated that LLMs can assist an embodied agent in solving complex sequential decision making tasks by providing high-level instructions. However, interactions with LLMs can be time-consuming. In many practical scenarios, it requires a significant amount of storage space that can only be deployed on remote cloud servers. Additionally, using commercial LLMs can be costly since they may charge based on usage frequency. In this paper, we explore how to enable intelligent cost-effective interactions between a down stream task oriented agent and an LLM. We find that this problem can be naturally formulated by a Markov decision process (MDP), and propose When2Ask, a reinforcement learning based approach that learns when it is necessary to query LLMs for high-level instructions to accomplish a target task. On one side, When2Ask discourages unnecessary redundant interactions, while on the other side, it enables the agent to identify and follow useful instructions from the LLM. This enables the agent to halt an ongoing plan and transition to a more suitable one based on new environmental observations. Experiments on MiniGrid and Habitat environments that entail planning sub-goals demonstrate that When2Ask learns to solve target tasks with only a few necessary interactions with the LLM, significantly reducing interaction costs in testing environments compared with baseline methods. Our code is available at: https: //github. com/ZJLAB-AMMI/LLM4RL.

IJCAI Conference 2024 Conference Paper

Meta-Learning via PAC-Bayesian with Data-Dependent Prior: Generalization Bounds from Local Entropy

• Shiyu Liu
• Wei Shi
• Zenglin Xu
• Shaogao Lv
• Yehong Zhang
• Hui Wang

Meta-learning accelerates the learning process on unseen learning tasks by acquiring prior knowledge through previous related tasks. The PAC-Bayesian theory provides a theoretical framework to analyze the generalization of meta-learning to unseen tasks. However, previous works still encounter two notable limitations: (1) they merely focus on the data-free priors, which often result in inappropriate regularization and loose generalization bounds; (2) more importantly, their optimization process usually involves nested optimization problems, incurring significant computational costs. To address these issues, we derive new generalization bounds and introduce a novel PAC-Bayesian framework for meta-learning that integrates data-dependent priors. This framework enables the extraction of optimal posteriors for each task in closed form, thereby allowing us to minimize generalization bounds incorporated data-dependent priors with only a simple local entropy. The resulting algorithm, which employs SGLD for sampling from the optimal posteriors, is stable, efficient, and computationally lightweight, eliminating the need for nested optimization. Extensive experimental results demonstrate that our proposed method outperforms the other baselines.

AAAI Conference 2024 Conference Paper

OctOcc: High-Resolution 3D Occupancy Prediction with Octree

• Wenzhe Ouyang
• Xiaolin Song
• Bailan Feng
• Zenglin Xu

3D semantic occupancy has garnered considerable attention due to its abundant structural information encompassing the entire scene in autonomous driving. However, existing 3D occupancy prediction methods contend with the constraint of low-resolution 3D voxel features arising from the limitation of computational memory. To address this limitation and achieve a more fine-grained representation of 3D scenes, we propose OctOcc, a novel octree-based approach for 3D semantic occupancy prediction. OctOcc is conceptually rooted in the observation that the vast majority of 3D space is left unoccupied. Capitalizing on this insight, we endeavor to cultivate memory-efficient high-resolution 3D occupancy predictions by mitigating superfluous cross-attentions. Specifically, we devise a hierarchical octree structure that selectively generates finer-grained cross-attentions solely in potentially occupied regions. Extending our inquiry beyond 3D space, we identify analogous redundancies within another side of cross attentions, 2D images. Consequently, a 2D image feature filtering network is conceived to expunge extraneous regions. Experimental results demonstrate that the proposed OctOcc significantly outperforms existing methods on nuScenes and SemanticKITTI datasets with limited memory consumption.

NeurIPS Conference 2024 Conference Paper

On the Necessity of Collaboration for Online Model Selection with Decentralized Data

• Junfan Li
• Zheshun Wu
• Zenglin Xu
• Irwin King

We consider online model selection with decentralized data over $M$ clients, and study the necessity of collaboration among clients. Previous work proposed various federated algorithms without demonstrating their necessity, while we answer the question from a novel perspective of computational constraints. We prove lower bounds on the regret, and propose a federated algorithm and analyze the upper bound. Our results show (i) collaboration is unnecessary in the absence of computational constraints on clients; (ii) collaboration is necessary if the computational cost on each client is limited to $o(K)$, where $K$ is the number of candidate hypothesis spaces. We clarify the unnecessary nature of collaboration in previous federated algorithms for distributed online multi-kernel learning, and improve the regret bounds at a smaller computational and communication cost. Our algorithm relies on three new techniques including an improved Bernstein's inequality for martingale, a federated online mirror descent framework, and decoupling model selection and prediction, which might be of independent interest.

AAAI Conference 2024 Conference Paper

Preparing Lessons for Progressive Training on Language Models

• Yu Pan
• Ye Yuan
• Yichun Yin
• Jiaxin Shi
• Zenglin Xu
• Ming Zhang
• Lifeng Shang
• Xin Jiang

The rapid progress of Transformers in artificial intelligence has come at the cost of increased resource consumption and greenhouse gas emissions due to growing model sizes. Prior work suggests using pretrained small models to improve training efficiency, but this approach may not be suitable for new model structures. On the other hand, training from scratch can be slow, and progressively stacking layers often fails to achieve significant acceleration. To address these challenges, we propose a novel method called Apollo, which prepares lessons for expanding operations by learning high-layer functionality during training of low layers. Our approach involves low-value-prioritized sampling (LVPS) to train different depths and weight sharing to facilitate efficient expansion. We also introduce an interpolation method for stable model depth extension. Experiments demonstrate that Apollo achieves state-of-the-art acceleration ratios, even rivaling methods using pretrained models, making it a universal and efficient solution for training deep models while reducing time, financial, and environmental costs.

ICML Conference 2024 Conference Paper

Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs

• Langzhang Liang
• Sunwoo Kim 0006
• Kijung Shin
• Zenglin Xu
• Shirui Pan
• Yuan (Alan) Qi

Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in heterophilic graphs, where adjacent nodes exhibit dissimilar labels or features, Signed Message Passing (SMP) has been widely adopted. However, there is a lack of theoretical and empirical analysis regarding the limitations of SMP. In this work, we unveil the potential pitfalls of SMP and their remedies. We first identify two limitations of SMP: undesirable representation update for multi-hop neighbors and vulnerability against oversmoothing issues. To overcome these challenges, we propose a novel message-passing function called Multiset to Multiset GNN (M2M-GNN). Our theoretical analyses and extensive experiments demonstrate that M2M-GNN effectively alleviates the limitations of SMP, yielding superior performance in comparison.

JMLR Journal 2023 Journal Article

FedLab: A Flexible Federated Learning Framework

• Dun Zeng
• Siqi Liang
• Xiangjing Hu
• Hui Wang
• Zenglin Xu

FedLab is a lightweight open-source framework for the simulation of federated learning. The design of FedLab focuses on federated learning algorithm effectiveness and communication efficiency. It allows customization on server optimization, client optimization, communication agreement, and communication compression. Also, FedLab is scalable in different deployment scenarios with different computation and communication resources. We hope FedLab could provide flexible APIs as well as reliable baseline implementations and relieve the burden of implementing novel approaches for researchers in the FL community. The source code, tutorial, and documentation can be found at https://github.com/SMILELab-FL/FedLab. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

UAI Conference 2023 Conference Paper

Practical privacy-preserving Gaussian process regression via secret sharing

• Jinglong Luo
• Yehong Zhang
• Jiaqi Zhang
• Shuang Qin
• Hui Wang 0013
• Yue Yu 0001
• Zenglin Xu

Gaussian process regression (GPR) is a non-parametric model that has been used in many real-world applications that involve sensitive personal data (e. g. , healthcare, finance, etc.) from multiple data owners. To fully and securely exploit the value of different data sources, this paper proposes a privacy-preserving GPR method based on secret sharing (SS), a secure multi-party computation (SMPC) technique. In contrast to existing studies that protect the data privacy of GPR via homomorphic encryption, differential privacy, or federated learning, our proposed method is more practical and can be used to preserve the data privacy of both the model inputs and outputs for various data-sharing scenarios (e. g. , horizontally/vertically-partitioned data). However, it is non-trivial to directly apply SS on the conventional GPR algorithm, as it includes some operations whose accuracy and/or efficiency have not been well-enhanced in the current SMPC protocol. To address this issue, we derive a new SS-based exponentiation operation through the idea of “confusion-correction” and construct an SS-based matrix inversion algorithm based on Cholesky decomposition. More importantly, we theoretically analyze the communication cost and the security of the proposed SS-based operations. Empirical results show that our proposed method can achieve reasonable accuracy and efficiency under the premise of preserving data privacy.

NeurIPS Conference 2023 Conference Paper

Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily

• Langzhang Liang
• Xiangjing Hu
• Zenglin Xu
• Zixing Song
• Irwin King

Graph Neural Networks (GNNs) have been shown to achieve remarkable performance on node classification tasks by exploiting both graph structures and node features. The majority of existing GNNs rely on the implicit homophily assumption. Recent studies have demonstrated that GNNs may struggle to model heterophilous graphs where nodes with different labels are more likely connected. To address this issue, we propose a generic GNN applicable to both homophilous and heterophilous graphs, namely Low-Rank Graph Neural Network (LRGNN). Our analysis demonstrates that a signed graph's global label relationship matrix has a low rank. This insight inspires us to predict the label relationship matrix by solving a robust low-rank matrix approximation problem, as prior research has proven that low-rank approximation could achieve perfect recovery under certain conditions. The experimental results reveal that the solution bears a strong resemblance to the label relationship matrix, presenting two advantages for graph modeling: a block diagonal structure and varying distributions of within-class and between-class entries.

IJCAI Conference 2023 Conference Paper

Prompt Learns Prompt: Exploring Knowledge-Aware Generative Prompt Collaboration For Video Captioning

• Liqi Yan
• Cheng Han
• Zenglin Xu
• Dongfang Liu
• Qifan Wang

Fine-tuning large vision-language models is a challenging task. Prompt tuning approaches have been introduced to learn fixed textual or visual prompts while freezing the pre-trained model in downstream tasks. Despite the effectiveness of prompt tuning, what do those learnable prompts learn remains unexplained. In this work, we explore whether prompts in the fine-tuning can learn knowledge-aware prompts from the pre-training, by designing two different sets of prompts in pre-training and fine-tuning phases respectively. Specifically, we present a Video-Language Prompt tuning (VL-Prompt) approach for video captioning, which first efficiently pre-train a video-language model to extract key information (e. g. , actions and objects) with flexibly generated Knowledge-Aware Prompt (KAP). Then, we design a Video-Language Prompt (VLP) to transfer the knowledge from the knowledge-aware prompts and fine-tune the model to generate full captions. Experimental results show the superior performance of our approach over several state-of-the-art baselines. We further demonstrate that the video-language prompts are well learned from the knowledge-aware prompts.

NeurIPS Conference 2023 Conference Paper

Reusing Pretrained Models by Multi-linear Operators for Efficient Training

• Yu Pan
• Ye Yuan
• Yichun Yin
• Zenglin Xu
• Lifeng Shang
• Xin Jiang
• Qun Liu

Training large models from scratch usually costs a substantial amount of resources. Towards this problem, recent studies such as bert2BERT and LiGO have reused small pretrained models to initialize a large model (termed the ``target model''), leading to a considerable acceleration in training. Despite the successes of these previous studies, they grew pretrained models by mapping partial weights only, ignoring potential correlations across the entire model. As we show in this paper, there are inter- and intra-interactions among the weights of both the pretrained and the target models. As a result, the partial mapping may not capture the complete information and lead to inadequate growth. In this paper, we propose a method that linearly correlates each weight of the target model to all the weights of the pretrained model to further enhance acceleration ability. We utilize multi-linear operators to reduce computational and spacial complexity, enabling acceptable resource requirements. Experiments demonstrate that our method can save 76\% computational costs on DeiT-base transferred from DeiT-small, which outperforms bert2BERT by +12\% and LiGO by +21\%, respectively.

AAAI Conference 2023 Conference Paper

Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering

• Zongmo Huang
• Yazhou Ren
• Xiaorong Pu
• Shudong Huang
• Zenglin Xu
• Lifang He

As one of the most important research topics in the unsupervised learning field, Multi-View Clustering (MVC) has been widely studied in the past decade and numerous MVC methods have been developed. Among these methods, the recently emerged Graph Neural Networks (GNN) shine a light on modeling both topological structure and node attributes in the form of graphs, to guide unified embedding learning and clustering. However, the effectiveness of existing GNN-based MVC methods is still limited due to the insufficient consideration in utilizing the self-supervised information and graph information, which can be reflected from the following two aspects: 1) most of these models merely use the self-supervised information to guide the feature learning and fail to realize that such information can be also applied in graph learning and sample weighting; 2) the usage of graph information is generally limited to the feature aggregation in these models, yet it also provides valuable evidence in detecting noisy samples. To this end, in this paper we propose Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering (SGDMC), which promotes the performance of GNN-based deep MVC models by making full use of the self-supervised information and graph information. Specifically, a novel attention-allocating approach that considers both the similarity of node attributes and the self-supervised information is developed to comprehensively evaluate the relevance among different nodes. Meanwhile, to alleviate the negative impact caused by noisy samples and the discrepancy of cluster structures, we further design a sample-weighting strategy based on the attention graph as well as the discrepancy between the global pseudo-labels and the local cluster assignment. Experimental results on multiple real-world datasets demonstrate the effectiveness of our method over existing approaches.

IJCAI Conference 2023 Conference Paper

SMARTformer: Semi-Autoregressive Transformer with Efficient Integrated Window Attention for Long Time Series Forecasting

• Yiduo Li
• Shiyi Qi
• Zhe Li
• Zhongwen Rao
• Lujia Pan
• Zenglin Xu

The success of Transformers in long time series forecasting (LTSF) can be attributed to their attention mechanisms and non-autoregressive (NAR) decoder structures, which capture long-range de- pendencies. However, time series data also contain abundant local temporal dependencies, which are often overlooked in the literature and significantly hinder forecasting performance. To address this issue, we introduce SMARTformer, which stands for SeMi-AutoRegressive Transformer. SMARTformer utilizes the Integrated Window Attention (IWA) and Semi-AutoRegressive (SAR) Decoder to capture global and local dependencies from both encoder and decoder perspectives. IWA conducts local self-attention in multi-scale windows and global attention across windows with linear com- plexity to achieve complementary clues in local and enlarged receptive fields. SAR generates subsequences iteratively, similar to autoregressive (AR) decoding, but refines the entire sequence in a NAR manner. This way, SAR benefits from both the global horizon of NAR and the local detail capturing of AR. We also introduce the Time-Independent Embedding (TIE), which better captures local dependencies by avoiding entanglements of various periods that can occur when directly adding po- sitional embedding to value embedding. Our ex- tensive experiments on five benchmark datasets demonstrate the effectiveness of SMARTformer against state-of-the-art models, achieving an improvement of 10. 2% and 18. 4% in multivariate and univariate long-term forecasting, respectively.

ICML Conference 2022 Conference Paper

A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

• Yu Pan 0005
• Zeyong Su
• Ao Liu 0008
• Jingquan Wang
• Nannan Li 0001
• Zenglin Xu

Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However, exploration of TCNNs is hindered even from weight initialization methods. To be specific, general initialization methods, such as Xavier or Kaiming initialization, usually fail to generate appropriate weights for TCNNs. Meanwhile, although there are ad-hoc approaches for specific architectures (e. g. , Tensor Ring Nets), they are not applicable to TCNNs with other tensor decomposition methods (e. g. , CP or Tucker decomposition). To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs. Specifically, we first present the Reproducing Transformation to convert the backward process in TCNNs to an equivalent convolution process. Then, based on the convolution operators in the forward and backward processes, we build a unified paradigm to control the variance of features and gradients in TCNNs. Thus, we can derive fan-in and fan-out initialization for various TCNNs. We demonstrate that our paradigm can stabilize the training of TCNNs, leading to faster convergence and better results.

NeurIPS Conference 2022 Conference Paper

Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid

• Jing Xu
• Xu Luo
• Xinglin Pan
• Yanan Li
• Wenjie Pei
• Zenglin Xu

Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning methods, the sample selection bias problem, i. e. , the sensitivity to the limited amount of support data, has not been well understood. In this paper, we find that this problem usually occurs when the positions of support samples are in the vicinity of task centroid—the mean of all class centroids in the task. This motivates us to propose an extremely simple feature transformation to alleviate this problem, dubbed Task Centroid Projection Removing (TCPR). TCPR is applied directly to all image features in a given task, aiming at removing the dimension of features along the direction of the task centroid. While the exact task centoid cannot be accurately obtained from limited data, we estimate it using base features that are each similar to one of the support features. Our method effectively prevents features from being too close to the task centroid. Extensive experiments over ten datasets from different domains show that TCPR can reliably improve classification accuracy across various feature extractors, training algorithms and datasets. The code has been made available at https: //github. com/KikimorMay/FSL-TCBR.

ICML Conference 2022 Conference Paper

Channel Importance Matters in Few-Shot Image Classification

• Xu Luo 0003
• Jing Xu
• Zenglin Xu

Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new classification tasks with a shift in task distribution. Understanding the difficulties posed by this task distribution shift is central to FSL. In this paper, we show that a simple channel-wise feature transformation may be the key to unraveling this secret from a channel perspective. When facing novel few-shot tasks in the test-time datasets, this transformation can greatly improve the generalization ability of learned image representations, while being agnostic to the choice of datasets and training algorithms. Through an in-depth analysis of this transformation, we find that the difficulty of representation transfer in FSL stems from the severe channel bias problem of image representations: channels may have different importance in different tasks, while convolutional neural networks are likely to be insensitive, or respond incorrectly to such a shift. This points out a core problem of the generalization ability of modern vision systems which needs further attention in the future.

IJCAI Conference 2022 Conference Paper

Contrastive Multi-view Hyperbolic Hierarchical Clustering

• Fangfei Lin
• Bing Bai
• Kun Bai
• Yazhou Ren
• Peng Zhao
• Zenglin Xu

Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i. e. , multi-view hierarchical clustering, to better understand the hierarchical structure of multi-view data. To this end, we propose a novel neural network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical Clustering(CMHHC). It consists of three components, i. e. , multi-view alignment learning, aligned feature similarity learning, and continuous hyperbolic hierarchical clustering. First, we align sample-level representations across multiple views in a contrastive way to capture the view-invariance information. Next, we utilize both the manifold and Euclidean similarities to improve the metric property. Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss. Finally, a binary clustering tree is decoded from optimized hyperbolic embeddings. Experimental results on five real-world datasets demonstrate the effectiveness of the proposed method and its components.

AAAI Conference 2022 Conference Paper

Multi-View Clustering on Topological Manifold

• Shudong Huang
• Ivor Tsang
• Zenglin Xu
• Jiancheng Lv
• Quan-Hui Liu

Multi-view clustering has received a lot of attentions in data mining recently. Though plenty of works have been investigated on this topic, it is still a severe challenge due to the complex nature of the multiple heterogeneous features. Particularly, existing multi-view clustering algorithms fail to consider the topological structure in the data, which is essential for clustering data on manifold. In this paper, we propose to exploit the implied data manifold by learning the topological relationship between data points. Our method coalesces multiple view-wise graphs with the topological relevance considered, and learns the weights as well as the consensus graph interactively in a unified framework. Furthermore, we manipulate the consensus graph by a connectivity constraint such that the data points from the same cluster are precisely connected into the same component. Substantial experiments on benchmark datasets are conducted to validate the effectiveness of the proposed method, compared to the state-of-the-art algorithms over the clustering performance.

NeurIPS Conference 2022 Conference Paper

Multi-view Subspace Clustering on Topological Manifold

• Shudong Huang
• Hongjie Wu
• Yazhou Ren
• Ivor Tsang
• Zenglin Xu
• Wentao Feng
• Jiancheng Lv

Multi-view subspace clustering aims to exploit a common affinity representation by means of self-expression. Plenty of works have been presented to boost the clustering performance, yet seldom considering the topological structure in data, which is crucial for clustering data on manifold. Orthogonal to existing works, in this paper, we argue that it is beneficial to explore the implied data manifold by learning the topological relationship between data points. Our model seamlessly integrates multiple affinity graphs into a consensus one with the topological relevance considered. Meanwhile, we manipulate the consensus graph by a connectivity constraint such that the connected components precisely indicate different clusters. Hence our model is able to directly obtain the final clustering result without reliance on any label discretization strategy as previous methods do. Experimental results on several benchmark datasets illustrate the effectiveness of the proposed model, compared to the state-of-the-art competitors over the clustering performance.

NeurIPS Conference 2021 Conference Paper

ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE

• Qingzhong Ai
• Lirong He
• Shiyu Liu
• Zenglin Xu

Recent studies show that advanced priors play a major role in deep generative models. Exemplar VAE, as a variant of VAE with an exemplar-based prior, has achieved impressive results. However, due to the nature of model design, an exemplar-based model usually requires vast amounts of data to participate in training, which leads to huge computational complexity. To address this issue, we propose Bayesian Pseudocoresets Exemplar VAE (ByPE-VAE), a new variant of VAE with a prior based on Bayesian pseudocoreset. The proposed prior is conditioned on a small-scale pseudocoreset rather than the whole dataset for reducing the computational cost and avoiding overfitting. Simultaneously, we obtain the optimal pseudocoreset via a stochastic optimization algorithm during VAE training aiming to minimize the Kullback-Leibler divergence between the prior based on the pseudocoreset and that based on the whole dataset. Experimental results show that ByPE-VAE can achieve competitive improvements over the state-of-the-art VAEs in the tasks of density estimation, representation learning, and generative data augmentation. Particularly, on a basic VAE architecture, ByPE-VAE is up to 3 times faster than Exemplar VAE while almost holding the performance. Code is available at \url{https: //github. com/Aiqz/ByPE-VAE}.

NeurIPS Conference 2021 Conference Paper

Rectifying the Shortcut Learning of Background for Few-Shot Learning

• Xu Luo
• Longhui Wei
• Liangjian Wen
• Jinrong Yang
• Lingxi Xie
• Zenglin Xu
• Qi Tian

The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time empirically identify image background, common in realistic images, as a shortcut knowledge helpful for in-class classification but ungeneralizable beyond training categories in FSL. A novel framework, COSOC, is designed to tackle this problem by extracting foreground objects in images at both training and evaluation without any extra supervision. Extensive experiments carried on inductive FSL tasks demonstrate the effectiveness of our approaches.

AAAI Conference 2020 Conference Paper

Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis

• Yong Dai
• Jian Liu
• Xiancong Ren
• Zenglin Xu

Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised information. Existing algorithms of MS-UDA either only exploit the shared features, i. e. , the domain-invariant information, or based on some weak assumption in NLP, e. g. , smoothness assumption. To avoid these problems, we propose two transfer learning frameworks based on the multi-source domain adaptation methodology for SA by combining the source hypotheses to derive a good target hypothesis. The key feature of the first framework is a novel Weighting Scheme based Unsupervised Domain Adaptation framework (WS-UDA), which combine the source classifiers to acquire pseudo labels for target instances directly. While the second framework is a Two-Stage Training based Unsupervised Domain Adaptation framework (2ST-UDA), which further exploits these pseudo labels to train a target private extractor. Importantly, the weights assigned to each source classifier are based on the relations between target instances and source domains, which measured by a discriminator through the adversarial training. Furthermore, through the same discriminator, we also fulfill the separation of shared features and private features. Experimental results on two SA datasets demonstrate the promising performance of our frameworks, which outperforms unsupervised state-of-the-art competitors.

AAAI Conference 2020 Conference Paper

Large-Scale Multi-View Subspace Clustering in Linear Time

• Zhao Kang
• Wangtao Zhou
• Zhitong Zhao
• Junming Shao
• Meng Han
• Zenglin Xu

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various largescale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.

ICLR Conference 2020 Conference Paper

Mutual Information Gradient Estimation for Representation Learning

• Liangjian Wen
• Yiji Zhou
• Lirong He
• Mingyuan Zhou
• Zenglin Xu

Mutual Information (MI) plays an important role in representation learning. However, MI is unfortunately intractable in continuous and high-dimensional settings. Recent advances establish tractable and scalable MI estimators to discover useful representation. However, most of the existing methods are not capable of providing an accurate estimation of MI with low-variance when the MI is large. We argue that directly estimating the gradients of MI is more appealing for representation learning than estimating MI in itself. To this end, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on the score estimation of implicit distributions. MIGE exhibits a tight and smooth gradient estimation of MI in the high-dimensional and large-MI settings. We expand the applications of MIGE in both unsupervised learning of deep representations based on InfoMax and the Information Bottleneck method. Experimental results have indicated significant performance improvement in learning useful representation.

AAAI Conference 2019 Conference Paper

Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

• Yu Pan
• Jing Xu
• Maolin Wang
• Jinmian Ye
• Fei Wang
• Kun Bai
• Zenglin Xu

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs very difficult. To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation. Compared with other tensor decomposition methods, TR-LSTM is more stable. In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data. Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensor-train LSTM and other state-of-the-art competitors.

IJCAI Conference 2019 Conference Paper

Multiple Partitions Aligned Clustering

• Zhao Kang
• Zipeng Guo
• Shudong Huang
• Siying Wang
• Wenyu Chen
• Yuanzhang Su
• Zenglin Xu

Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information loss because of unavoidable noise or inconsistency among views. Since different views admit the same cluster structure, the natural space should be all partitions. Orthogonal to existing techniques, in this paper, we propose to leverage the multi-view information by fusing partitions. Specifically, we align each partition to form a consensus cluster indicator matrix through a distinct rotation matrix. Moreover, a weight is assigned for each view to account for the clustering capacity differences of views. Finally, the basic partitions, weights, and consensus clustering are jointly learned in a unified framework. We demonstrate the effectiveness of our approach on several real datasets, where significant improvement is found over other state-of-the-art multi-view clustering methods.

AAAI Conference 2019 Conference Paper

Similarity Learning via Kernel Preserving Embedding

• Zhao Kang
• Yiwei Lu
• Yuanzhang Su
• Changsheng Li
• Zenglin Xu

Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semisupervised learning. However, it just tries to reconstruct the original data and some valuable information, e. g. , the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-ofthe-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.

AAAI Conference 2018 Conference Paper

Learning With Incomplete Labels

• Yingming Li
• Zenglin Xu
• Zhongfei Zhang

For many real-world tagging problems, training labels are usually obtained through social tagging and are notoriously incomplete. Consequently, handling data with incomplete labels has become a difficult challenge, which usually leads to a degenerated performance on label prediction. To improve the generalization performance, in this paper, we first propose the Improved Cross-View learning (referred as ICVL) model, which considers both global and local patterns of label relationship to enrich the original label set. Further, by extending the ICVL model with an outlier detection mechanism, we introduce the Improved Cross-View learning with Outlier Detection (referred as ICVL-OD) model to remove the abnormal tags resulting from label enrichment. Extensive evaluations on three benchmark datasets demonstrate that ICVL and ICVL-OD outstand with superior performances in comparison with the competing methods.

IJCAI Conference 2018 Conference Paper

Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

• Zhao Kang
• Xiao Lu
• Jinfeng Yi
• Zenglin Xu

Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms. In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight. Impressively, the proposed method can automatically assign an appropriate weight to each kernel without introducing additional parameters, as existing methods do. The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on multiple benchmark datasets and our empirical results verify the superiority of the proposed framework.

IJCAI Conference 2018 Conference Paper

Structured Inference for Recurrent Hidden Semi-markov Model

• Hao Liu
• Lirong He
• Haoli Bai
• Bo Dai
• Kun Bai
• Zenglin Xu

Segmentation and labeling for high dimensional time series is an important yet challenging task in a number of applications, such as behavior understanding and medical diagnosis. Recent advances to model the nonlinear dynamics in such time series data, has suggested to involve recurrent neural networks into Hidden Markov Models. However, this involvement has caused the inference procedure much more complicated, often leading to intractable inference, especially for the discrete variables of segmentation and labeling. To achieve both flexibility and tractability in modeling nonlinear dynamics of discrete variables, we present a structured and stochastic sequential neural network (SSNN), which composes with a generative network and an inference network. In detail, the generative network aims to not only capture the long-term dependencies but also model the uncertainty of the segmentation labels via semi-Markov models. More importantly, for efficient and accurate inference, the proposed bi-directional inference network reparameterizes the categorical segmentation with the Gumbel-Softmax approximation and resorts to the Stochastic Gradient Variational Bayes. We evaluate the proposed model in a number of tasks, including speech modeling, automatic segmentation and labeling in behavior understanding, and sequential multi-objects recognition. Experimental results have demonstrated that our proposed model can achieve significant improvement over the state-of-the-art methods.

AAAI Conference 2018 Conference Paper

Unified Spectral Clustering With Optimal Graph

• Zhao Kang
• Chong Peng
• Qiang Cheng
• Zenglin Xu

Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to severe information loss and performance degradation. First, predefined similarity graph might not be optimal for subsequent clustering. It is well-accepted that similarity graph highly affects the clustering results. To this end, we propose to automatically learn similarity information from data and simultaneously consider the constraint that the similarity matrix has exact c connected components if there are c clusters. Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers. In this work, we transform the candidate solution into a new one that better approximates the discrete one. Finally, those three subtasks are integrated into a unified framework, with each subtask iteratively boosted by using the results of the others towards an overall optimal solution. It is known that the performance of a kernel method is largely determined by the choice of kernels. To tackle this practical problem of how to select the most suitable kernel for a particular data set, we further extend our model to incorporate multiple kernel learning ability. Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches.

AAAI Conference 2017 Conference Paper

Learning with Feature Network and Label Network Simultaneously

• Yingming Li
• Ming Yang
• Zenglin Xu
• Zhongfei (Mark) Zhang

For many supervised learning problems, limited training samples and incomplete labels are two difficult challenges, which usually lead to degenerated performance on label prediction. To improve the generalization performance, in this paper, we propose Doubly Regularized Multi-Label learning (DRML) by exploiting feature network and label network regularization simultaneously. In more details, the proposed algorithm first constructs a feature network and a label network with marginalized linear denoising autoencoder in data feature set and label set, respectively, and then learns a robust predictor with the feature network and the label network regularization simultaneously. While DRML is a general method for multilabel learning, in the evaluations we focus on the specific application of multi-label text tagging. Extensive evaluations on three benchmark data sets demonstrate that DRML outstands with a superior performance in comparison with some existing multi-label learning methods.

IJCAI Conference 2017 Conference Paper

Robust Softmax Regression for Multi-class Classification with Self-Paced Learning

• Yazhou Ren
• Peng Zhao
• Yongpan Sheng
• Dezhong Yao
• Zenglin Xu

Softmax regression, a generalization of Logistic regression (LR) in the setting of multi-class classification, has been widely used in many machine learning applications. However, the performance of softmax regression is extremely sensitive to the presence of noisy data and outliers. To address this issue, we propose a model of robust softmax regression (RoSR) originated from the self-paced learning (SPL) paradigm for multi-class classification. Concretely, RoSR equipped with the soft weighting scheme is able to evaluate the importance of each data instance. Then, data instances participate in the classification problem according to their weights. In this way, the influence of noisy data and outliers (which are typically with small weights) can be significantly reduced. However, standard SPL may suffer from the imbalanced class influence problem, where some classes may have little influence in the training process if their instances are not sensitive to the loss. To alleviate this problem, we design two novel soft weighting schemes that assign weights and select instances locally for each class. Experimental results demonstrate the effectiveness of the proposed methods.

JAIR Journal 2016 Journal Article

Association Discovery and Diagnosis of Alzheimer’s Disease with Bayesian Multiview Learning

• Zenglin Xu
• Shandian Zhe
• Yuan Qi
• Peng Yu

The analysis and diagnosis of Alzheimer’s disease (AD) can be based on genetic variations, e.g., single nucleotide polymorphisms (SNPs) and phenotypic traits, e.g., Magnetic Resonance Imaging (MRI) features. We consider two important and related tasks: i) to select genetic and phenotypical markers for AD diagnosis and ii) to identify associations between genetic and phenotypical data. While previous studies treat these two tasks separately, they are tightly coupled because underlying associations between genetic variations and phenotypical features contain the biological basis for a disease. Here we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels; in return, the disease status can guide the discovery of relationships between data sources. The sparse projection matrices not only reveal interactions between data sources but also select groups of biomarkers related to the disease. Moreover, to take advantage of the linkage disequilibrium (LD) measuring the non-random association of alleles, we incorporate a graph Laplacian type of prior in the model. To learn the model from data, we develop an efficient variational inference algorithm. Analysis on an imaging genetics dataset for the study of Alzheimer’s Disease (AD) indicates that our model identifies biologically meaningful associations between genetic variations and MRI features, and achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.

AAAI Conference 2016 Conference Paper

DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays

• Shandian Zhe
• Yuan Qi
• Youngja Park
• Zenglin Xu
• Ian Molloy
• Suresh Chari

Tensor decomposition methods are effective tools for modelling multidimensional array data (i. e. , tensors). Among them, nonparametric Bayesian models, such as Infinite Tucker Decomposition (InfTucker), are more powerful than multilinear factorization approaches, including Tucker and PARAFAC, and usually achieve better predictive performance. However, they are difficult to handle massive data due to a prohibitively high training cost. To address this limitation, we propose Distributed infinite Tucker (DINTUCKER), a new hierarchical Bayesian model that enables local learning of InfTucker on subarrays and global information integration from local results. We further develop a distributed stochastic gradient descent algorithm, coupled with variational inference for model estimation. In addition, the connection between DINTUCKER and InfTucker is revealed in terms of model evidence. Experiments demonstrate that DINTUCKER maintains the predictive accuracy of InfTucker and is scalable on massive data: On multidimensional arrays with billions of elements from two real-world applications, DINTUCKER achieves significantly higher prediction accuracy with less training time, compared with the state-of-the-art large-scale tensor decomposition method, GigaTensor.

NeurIPS Conference 2016 Conference Paper

Distributed Flexible Nonlinear Tensor Factorization

• Shandian Zhe
• Kai Zhang
• Pengyuan Wang
• Kuang-chih Lee
• Zenglin Xu
• Yuan Qi
• Zoubin Ghahramani

Tensor factorization is a powerful tool to analyse multi-way data. Recently proposed nonlinear factorization methods, although capable of capturing complex relationships, are computationally quite expensive and may suffer a severe learning bias in case of extreme data sparsity. Therefore, we propose a distributed, flexible nonlinear tensor factorization model, which avoids the expensive computations and structural restrictions of the Kronecker-product in the existing TGP formulations, allowing an arbitrary subset of tensor entries to be selected for training. Meanwhile, we derive a tractable and tight variational evidence lower bound (ELBO) that enables highly decoupled, parallel computations and high-quality inference. Based on the new bound, we develop a distributed, key-value-free inference algorithm in the MapReduce framework, which can fully exploit the memory cache mechanism in fast MapReduce systems such as Spark. Experiments demonstrate the advantages of our method over several state-of-the-art approaches, in terms of both predictive performance and computational efficiency.

AAAI Conference 2016 Conference Paper

Learning with Marginalized Corrupted Features and Labels Together

• Yingming Li
• Ming Yang
• Zenglin Xu
• Zhongfei Zhang

Tagging has become increasingly important in many real-world applications noticeably including web applications, such as web blogs and resource sharing systems. Despite this importance, tagging methods often face difficult challenges such as limited training samples and incomplete labels, which usually lead to degenerated performance on tag prediction. To improve the generalization performance, in this paper, we propose Regularized Marginalized Cross-View learning (RMCV) by jointly modeling on attribute noise and label noise. In more details, the proposed model constructs infinite training examples with attribute noises from known exponential-family distributions and exploits label noise via marginalized denoising autoencoder. Therefore, the model benefits from its robustness and alleviates the problem of tag sparsity. While RMCV is a general method for learning tagging, in the evaluations we focus on the specific application of multi-label text tagging. Extensive evaluations on three benchmark data sets demonstrate that RMCV outstands with a superior performance in comparison with state-of-the-art methods.

IJCAI Conference 2016 Conference Paper

Multi-View Learning with Limited and Noisy Tagging

• Yingming Li
• Ming Yang
• Zenglin Xu
• Zhongfei (Mark) Zhang

Multi-view tagging has become increasingly popular in the applications where data representations by multiple views exist. A robust multi-view tagging method must have the capability to meet the two challenging requirements: limited labeled training samples and noisy labeled training samples. In this paper, we investigate this challenging problem of learning with limited and noisy tagging and propose a discriminative model, called MSMC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label space consistency into the optimization. While MSMC is a general method for learning with multi-view, limited, and noisy tagging, in the evaluations we focus on the specific application of noisy image tagging with limited labeled training samples on a benchmark dataset. Extensive evaluations in comparison with state-of-the-art literature demonstrate that MSMC outstands with a superior performance.

AAAI Conference 2015 Conference Paper

Nystrom Approximation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation

• Zenglin Xu
• Rong Jin
• Bin Shen
• Shenghuo Zhu

Nyström approximation is an effective approach to accelerate the computation of kernel matrices in many kernel methods. In this paper, we consider the Nyström approximation for sparse kernel methods. Instead of relying on the low-rank assumption of the original kernels, which sometimes does not hold in some applications, we take advantage of the restricted eigenvalue condition, which has been proved to be robust for sparse kernel methods. Based on the restricted eigenvalue condition, we have provided not only the approximation bound for the original kernel matrix but also the recovery bound for the sparse solutions of sparse kernel regression. In addition to the theoretical analysis, we also demonstrate the good performance of the Nyström approximation for sparse kernel regression on real world data sets.

AAAI Conference 2015 Conference Paper

Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer’s Disease

• Shandian Zhe
• Zenglin Xu
• Yuan Qi
• Peng Yu

In the analysis and diagnosis of many diseases, such as the Alzheimer’s disease (AD), two important and related tasks are usually required: i) selecting genetic and phenotypical markers for diagnosis, and ii) identifying associations between genetic and phenotypical features. While previous studies treat these two tasks separately, they are tightly coupled due to the same underlying biological basis. To harness their potential benefits for each other, we propose a new sparse Bayesian approach to jointly carry out the two important and related tasks. In our approach, we extract common latent features from different data sources by sparse projection matrices and then use the latent features to predict disease severity levels; in return, the disease status can guide the learning of sparse projection matrices, which not only reveal interactions between data sources but also select groups of related biomarkers. In order to boost the learning of sparse projection matrices, we further incorporate graph Laplacian priors encoding the valuable linkage disequilibrium (LD) information. To efficiently estimate the model, we develop a variational inference algorithm. Analysis on an imaging genetics dataset for AD study shows that our model discovers biologically meaningful associations between single nucleotide polymorphisms (SNPs) and magnetic resonance imaging (MRI) features, and achieves significantly higher accuracy for predicting ordinal AD stages than competitive methods.

NeurIPS Conference 2013 Conference Paper

Exact and Stable Recovery of Pairwise Interaction Tensors

• Shouyuan Chen
• Michael Lyu
• Irwin King
• Zenglin Xu

Tensor completion from incomplete observations is a problem of significant practical interest. However, it is unlikely that there exists an efficient algorithm with provable guarantee to recover a general tensor from a limited number of observations. In this paper, we study the recovery algorithm for pairwise interaction tensors, which has recently gained considerable attention for modeling multiple attribute data due to its simplicity and effectiveness. Specifically, in the absence of noise, we show that one can exactly recover a pairwise interaction tensor by solving a constrained convex program which minimizes the weighted sum of nuclear norms of matrices from $O(nr\log^2(n))$ observations. For the noisy cases, we also prove error bounds for a constrained convex program for recovering the tensors. Our experiments on the synthetic dataset demonstrate that the recovery performance of our algorithm agrees well with the theory. In addition, we apply our algorithm on a temporal collaborative filtering task and obtain state-of-the-art results.

ICML Conference 2012 Conference Paper

Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis

• Zenglin Xu
• Feng Yan 0003
• Yuan (Alan) Qi

Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches—such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)—amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entities, (ii) various data types (e. g. missing data and binary data), and (iii) noisy observations and outliers. To address these issues, we propose tensorvariate latent nonparametric Bayesian models, coupled with efficient inference methods, for multiway data analysis. We name these models InfTucker. Using these InfTucker models, we conduct Tucker decomposition in an infinite feature space. Unlike classical tensor decomposition models, our new approaches handle both continuous and binary data in a probabilistic framework. Unlike previous Bayesian models on matrices and tensors, our models are based on latent Gaussian or t processes with nonlinear covariance functions. To efficiently learn the InfTucker models from data, we develop a variational inference technique on tensors. Compared with classical implementation, the new technique reduces both time and space complexities by several orders of magnitude. Our experimental results on chemometrics and social network datasets demonstrate that our new models achieved significantly higher prediction accuracy than the most state-of-art tensor decomposition approaches. Appearing in Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012. Copyright 2012 by the author(s)/owner(s).

UAI Conference 2011 Conference Paper

Sparse matrix-variate Gaussian process blockmodels for network modeling

• Feng Yan 0003
• Zenglin Xu
• Yuan (Alan) Qi

We face network data from various sources, such as protein interactions and online social networks. A critical problem is to model network interactions and identify latent groups of network nodes. This problem is challenging due to many reasons. For example, the network nodes are interdependent instead of independent of each other, and the data are known to be very noisy (e.g., missing edges). To address these challenges, we propose a new relational model for network data, Sparse Matrix-variate Gaussian process Blockmodel (SMGB). Our model generalizes popular bilinear generative models and captures nonlinear network interactions using a matrix-variate Gaussian process with latent membership variables. We also assign sparse prior distributions on the latent membership variables to learn sparse group assignments for individual network nodes. To estimate the latent variables efficiently from data, we develop an efficient variational expectation maximization method. We compared our approaches with several state-of-the-art network models on both synthetic and real-world network datasets. Experimental results demonstrate SMGBs outperform the alternative approaches in terms of discovering latent classes or predicting unknown interactions.

AAAI Conference 2011 Conference Paper

Sparse Matrix-Variate t Process Blockmodels

• Zenglin Xu
• Feng Yan
• Yuan Qi

We consider the problem of modeling network interactions and identifying latent groups of network nodes. This problem is challenging due to the facts i) that the network nodes are interdependent instead of independent, ii) that the network data are very noisy (e. g. , missing edges), and iii) that the network interactions are often sparse. To address these challenges, we propose a Sparse Matrix-variate t process Blockmodel (SMTB). In particular, we generalize a matrix-variate t distribution to a t process on matrices with nonlinear covariance functions. Due to this generalization, our model can estimate latent memberships for individual network nodes. This separates our model from previous t distribution based relational models. Also, we introduce sparse prior distributions on the latent membership parameters to select group assignments for individual nodes. To learn the model efficiently from data, we develop a variational method. When compared with several state-of-the-art models, including the predictive matrixvariate t models and mixed membership stochastic blockmodels, our model achieved improved prediction accuracy on real world network datasets.

ICML Conference 2010 Conference Paper

Online Learning for Group Lasso

• Haiqin Yang
• Zenglin Xu
• Irwin King
• Michael R. Lyu

UAI Conference 2010 Conference Paper

Robust Metric Learning by Smooth Optimization

• Kaizhu Huang
• Rong Jin 0001
• Zenglin Xu
• Cheng-Lin Liu 0001

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users’ implicit feedbacks and citations among articles. As a result, these constraints are usually noisy and contain many mistakes. In this work, we aim to learn a distance metric from noisy constraints by robust optimization in a worst-case scenario, to which we refer as robust metric learning. We formulate the learning task initially as a combinatorial optimization problem, and show that it can be elegantly transformed to a convex programming problem. We present an efficient learning algorithm based on smooth optimization [7]. It has a worst-case convergence rate of O(1/ √ ε) for smooth optimization problems, where ε is the desired error of the approximate solution. Finally, our empirical study with UCI data sets demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods.

ICML Conference 2010 Conference Paper

Simple and Efficient Multiple Kernel Learning by Group Lasso

• Zenglin Xu
• Rong Jin 0001
• Haiqin Yang
• Irwin King
• Michael R. Lyu

AAAI Conference 2010 Conference Paper

Smooth Optimization for Effective Multiple Kernel Learning

• Zenglin Xu
• Rong Jin
• Shenghuo Zhu
• Michael Lyu
• Irwin King

Multiple Kernel Learning (MKL) can be formulated as a convex-concave minmax optimization problem, whose saddle point corresponds to the optimal solution to MKL. Most MKL methods employ the L1-norm simplex constraints on the combination weights of kernels, which therefore involves optimization of a non-smooth function of the kernel weights. These methods usually divide the optimization into two cycles: one cycle deals with the optimization on the kernel combination weights, and the other cycle updates the parameters of SVM. Despite the success of their efficiency, they tend to discard informative complementary kernels. To improve accuracy, we introduce smoothness to the optimization procedure. Furthermore, we transform the optimization into a single smooth convex optimization problem and employ the Nesterov’s method to efficiently solve the optimization problem. Experiments on benchmark data sets demonstrate that the proposed algorithm clearly improves current MKL methods in a number scenarios.

IJCAI Conference 2009 Conference Paper

• Zenglin Xu
• Rong Jin
• Michael R. Lyu
• Irwin King

We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data. To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. The proposed method selects features through maximizing the classification margin between different classes and simultaneously exploiting the geometry of the probability distribution that generates both labeled and unlabeled data. We formulate the proposed feature selection method into a convex-concave optimization problem, where the saddle point corresponds to the optimal solution. To find the optimal solution, the level method, a fairly recent optimization method, is employed. We also present a theoretic proof of the convergence rate for the application of the level method to our problem. Empirical evaluation on several benchmark data sets demonstrates the effectiveness of the proposed semi-supervised feature selection method.

NeurIPS Conference 2009 Conference Paper

Adaptive Regularization for Transductive Support Vector Machine

• Zenglin Xu
• Rong Jin
• Jianke Zhu
• Irwin King
• Michael Lyu
• Zhirong Yang

We discuss the framework of Transductive Support Vector Machine (TSVM) from the perspective of the regularization strength induced by the unlabeled data. In this framework, SVM and TSVM can be regarded as a learning machine without regularization and one with full regularization from the unlabeled data, respectively. Therefore, to supplement this framework of the regularization strength, it is necessary to introduce data-dependant partial regularization. To this end, we reformulate TSVM into a form with controllable regularization strength, which includes SVM and TSVM as special cases. Furthermore, we introduce a method of adaptive regularization that is data dependant and is based on the smoothness assumption. Experiments on a set of benchmark data sets indicate the promising results of the proposed work compared with state-of-the-art TSVM algorithms.

NeurIPS Conference 2009 Conference Paper

Heavy-Tailed Symmetric Stochastic Neighbor Embedding

• Zhirong Yang
• Irwin King
• Zenglin Xu
• Erkki Oja

Stochastic Neighbor Embedding (SNE) has shown to be quite promising for data visualization. Currently, the most popular implementation, t-SNE, is restricted to a particular Student t-distribution as its embedding distribution. Moreover, it uses a gradient descent algorithm that may require users to tune parameters such as the learning step size, momentum, etc. , in finding its optimum. In this paper, we propose the Heavy-tailed Symmetric Stochastic Neighbor Embedding (HSSNE) method, which is a generalization of the t-SNE to accommodate various heavy-tailed embedding similarity functions. With this generalization, we are presented with two difficulties. The first is how to select the best embedding similarity among all heavy-tailed functions and the second is how to optimize the objective function once the heave-tailed function has been selected. Our contributions then are: (1) we point out that various heavy-tailed embedding similarities can be characterized by their negative score functions. Based on this finding, we present a parameterized subset of similarity functions for choosing the best tail-heaviness for HSSNE; (2) we present a fixed-point optimization algorithm that can be applied to all heavy-tailed functions and does not require the user to set any parameters; and (3) we present two empirical studies, one for unsupervised visualization showing that our optimization algorithm runs as fast and as good as the best known t-SNE implementation and the other for semi-supervised visualization showing quantitative superiority using the homogeneity measure as well as qualitative advantage in cluster separation over t-SNE.

ICML Conference 2009 Conference Paper

Non-monotonic feature selection

• Zenglin Xu
• Rong Jin 0001
• Jieping Ye
• Michael R. Lyu
• Irwin King

NeurIPS Conference 2008 Conference Paper

An Extended Level Method for Efficient Multiple Kernel Learning

• Zenglin Xu
• Rong Jin
• Irwin King
• Michael Lyu

We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i. e. , Semi-Infinite Linear Programming (SILP) and Subgradient Descent (SD), have been proposed for large-scale multiple kernel learning. Despite their success, both methods have their own shortcomings: (a) the SD method utilizes the gradient of only the current solution, and (b) the SILP method does not regularize the approximate solution obtained from the cutting plane model. In this work, we extend the level method, which was originally designed for optimizing non-smooth objective functions, to convex-concave optimization, and apply it to multiple kernel learning. The extended level method overcomes the drawbacks of SILP and SD by exploiting all the gradients computed in past iterations and by regularizing the solution via a projection to a level set. Empirical study with eight UCI datasets shows that the extended level method can significantly improve efficiency by saving on average 91. 9% of computational time over the SILP method and 70. 3% over the SD method.

NeurIPS Conference 2007 Conference Paper

Efficient Convex Relaxation for Transductive Support Vector Machine

• Zenglin Xu
• Rong Jin
• Jianke Zhu
• Irwin King
• Michael Lyu

We consider the problem of Support Vector Machine transduction, which involves a combinatorial problem with exponential computational complexity in the number of unlabeled examples. Although several studies are devoted to Transductive SVM, they suffer either from the high computation complexity or from the solutions of local optimum. To address this problem, we propose solving Transductive SVM via a convex relaxation, which converts the NP-hard problem to a semi-definite programming. Compared with the other SDP relaxation for Transductive SVM, the proposed algorithm is computationally more efficient with the number of free parameters reduced from O(n2) to O(n) where n is the number of examples. Empirical study with several benchmark data sets shows the promising performance of the proposed algorithm in comparison with other state-of-the-art implementations of Transductive SVM.