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Charles Ling

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11 papers

1 author row

AAAI Conference 2026 Conference Paper

Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure

Ruiyi Fang
Shuo Wang
Ruizhi Pu
Qiuhao Zeng
Hao Zheng
Ziyan Wang
Jiale Cai
Zhimin Mei

Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods typically assume that both source and target graphs exhibit homophily, leading existing methods to perform poorly when heterophily is present. Furthermore, the lack of labels in the target graph makes it impossible to assess its homophily level beforehand. To address this challenge, we propose a novel homophily-agnostic approach that effectively transfers knowledge between graphs with varying degrees of homophily. Specifically, we adopt a divide-and-conquer strategy that first separately reconstructs highly homophilic and heterophilic variants of both the source and target graphs, and then performs knowledge alignment separately between corresponding graph variants. Extensive experiments conducted on five benchmark datasets demonstrate the superior performance of our approach, particularly highlighting its substantial advantages on heterophilic graphs.

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AAAI Conference 2025 Conference Paper

Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression

Ruizhi Pu
Gezheng Xu
Ruiyi Fang
Bing-Kun Bao
Charles Ling
Boyu Wang

Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning. While recent works have already shown that incorporating various classification-based regularizers can produce enhanced outcomes, the role of classification remains elusive in DIR. Moreover, such regularizers (e.g., contrastive penalties) merely focus on learning discriminative features of data, which inevitably results in ignorance of either continuity or similarity across the data. To address these issues, we first bridge the connection between the objectives of DIR and classification from a Bayesian perspective. Consequently, this motivates us to decompose the objective of DIR into a combination of classification and regression tasks, which naturally guides us toward a divide-and-conquer manner to solve the DIR problem. Specifically, by aggregating the data at nearby labels into the same groups, we introduce an ordinal group-aware contrastive learning loss along with a multi-experts regressor to tackle the different groups of data thereby maintaining the data continuity. Meanwhile, considering the similarity between the groups, we also propose a symmetric descending soft labeling strategy to exploit the intrinsic similarity across the data, which allows classification to facilitate regression more effectively. Extensive experiments on real-world datasets also validate the effectiveness of our method.

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AAAI Conference 2025 Conference Paper

MABR: Multilayer Adversarial Bias Removal Without Prior Bias Knowledge

Maxwell J. Yin
Boyu Wang
Charles Ling

Models trained on real-world data often mirror and exacerbate existing social biases. Traditional methods for mitigating these biases typically require prior knowledge of the specific biases to be addressed, and the social groups associated with each instance. In this paper, we introduce a novel adversarial training strategy that operates withour relying on prior bias-type knowledge (e.g., gender or racial bias) and protected attribute labels. Our approach dynamically identifies biases during model training by utilizing auxiliary bias detector. These detected biases are simultaneously mitigated through adversarial training. Crucially, we implement these bias detectors at various levels of the feature maps of the main model, enabling the detection of a broader and more nuanced range of bias features. Through experiments on racial and gender biases in sentiment and occupation classification tasks, our method effectively reduces social biases without the need for demographic annotations. Moreover, our approach not only matches but often surpasses the efficacy of methods that require detailed demographic insights, marking a significant advancement in bias mitigation techniques.

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AAAI Conference 2025 Conference Paper

Textualize Visual Prompt for Image Editing via Diffusion Bridge

Pengcheng Xu
Qingnan Fan
Fei Kou
Shuai Qin
Hong Gu
Ruoyu Zhao
Charles Ling
Boyu Wang

Visual prompt, a pair of before-and-after edited images, can convey indescribable imagery transformations and prosper in image editing. However, current visual prompt methods rely on a pretrained text-guided image-to-image generative model that requires a triplet of text, before, and after images for retraining over a text-to-image model. Such crafting triplets and retraining processes limit the scalability and generalization of editing. In this paper, we present a framework based on any single text-to-image model without reliance on the explicit image-to-image model thus enhancing the generalizability and scalability. Specifically, by leveraging the probability-flow ordinary equation, we construct a diffusion bridge to transfer the distribution between before-and-after images under the text guidance. By optimizing the text via the bridge, the framework adaptively textualizes the editing transformation conveyed by visual prompts into text embeddings without other models. Meanwhile, we introduce differential attention control during optimization, which disentangles the text embedding from the invariance of the before-and-after images and makes it solely capture the delicate transformation and generalize to edit various images. Experiments on real images validate competitive results on the generalization, contextual coherence, and high fidelity for delicate editing with just one image pair as the visual prompt.

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TMLR Journal 2025 Journal Article

Uniform Noise Distribution and Compact Clusters: Unveiling the Success of Self-Supervised Learning in Label Noise

Pengcheng Xu
Li Yi
Gezheng Xu
Xi Chen
Ian McLeod
Charles Ling
Boyu Wang

Label noise is ubiquitous in real-world datasets, posing significant challenges to machine learning models. While self-supervised learning (SSL) algorithms have empirically demonstrated effectiveness in learning noisy labels, the theoretical understanding of their effectiveness remains underexplored. In this paper, we present a theoretical framework to understand how SSL methods enhance learning with noisy labels, especially for the instance-dependent label noise. We reveal that the uniform and compact cluster structures induced by contrastive SSL play a crucial role in mitigating the adverse effects of label noise. Specifically, we theoretically show that a classifier trained on SSL-learned representations significantly outperforms one trained using traditional supervised learning methods. This results from two key merits of SSL representations over label noise: 1. Uniform Noise Distribution: Label noise becomes uniformly distributed over SSL representations with respect to the true class labels, rather than the noisy ones, leading to an easier learning task. 2. Enhanced Cluster Structure: SSL enhances the formation of well-separated and compact categorical clusters, increasing inter-class distances while tightening intra-class clusters. We further theoretically justify the benefits of training a classifier on such structured representations, demonstrating that it encourages the classifier trained on noisy data to be aligned with the optimal classifier. Extensive experiments validate the robustness of SSL representations in combating label noise, confirming the practical values of our theoretical findings.

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NeurIPS Conference 2025 Conference Paper

Versatile Transferable Unlearnable Example Generator

Zhihao Li
Jiale Cai
Gezheng Xu
Hao Zheng
Qiuyue Li
Fan Zhou
Shichun Yang
Charles Ling

The rapid growth of publicly available data has fueled deep learning advancements but also raises concerns about unauthorized data usage. Unlearnable Examples (UEs) have emerged as a data protection strategy that introduces imperceptible perturbations to prevent unauthorized learning. However, most existing UE methods produce perturbations strongly tied to specific training sets, leading to a significant drop in unlearnability when applied to unseen data or tasks. In this paper, we argue that for broad applicability, UEs should maintain their effectiveness across diverse application scenarios. To this end, we conduct the first comprehensive study on the transferability of UEs across diverse and practical yet demanding settings. Specifically, we identify key scenarios that pose significant challenges for existing UE methods, including varying styles, out-of-distribution classes, resolutions, and architectures. Moreover, we propose $\textbf{Versatile Transferable Generator}$ (VTG), a transferable generator designed to safeguard data across various conditions. Specifically, VTG integrates Adversarial Domain Augmentation (ADA) into the generator’s training process to synthesize out-of-distribution samples, thereby improving its generalizability to unseen scenarios. Furthermore, we propose a Perturbation-Label Coupling (PLC) mechanism that leverages contrastive learning to directly align perturbations with class labels. This approach reduces the generator’s reliance on data semantics, allowing VTG to produce unlearnable perturbations in a distribution-agnostic manner. Extensive experiments demonstrate the effectiveness and broad applicability of our approach. Code is available at https: //github. com/zhli-cs/VTG.

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NeurIPS Conference 2024 Conference Paper

Towards Understanding Evolving Patterns in Sequential Data

Qiuhao Zeng
Long-Kai Huang
Qi Chen
Charles Ling
Boyu Wang

In many machine learning tasks, data is inherently sequential. Most existing algorithms learn from sequential data in an auto-regressive manner, which predicts the next unseen data point based on the observed sequence, implicitly assuming the presence of an \emph{evolving pattern} embedded in the data that can be leveraged. However, identifying and assessing evolving patterns in learning tasks often relies on subjective judgments rooted in the prior knowledge of human experts, lacking a standardized quantitative measure. Furthermore, such measures enable us to determine the suitability of employing sequential models effectively and make informed decisions on the temporal order of time series data, and feature/data selection processes. To address this issue, we introduce the Evolving Rate (EvoRate), which quantitatively approximates the intensity of evolving patterns in the data with Mutual Information. Furthermore, in some temporal data with neural mutual information estimations, we only have snapshots at different timestamps, lacking correspondence, which hinders EvoRate estimation. To tackle this challenge, we propose EvoRate$_\mathcal{W}$, aiming to establish correspondence with optimal transport for estimating the first-order EvoRate. Experiments on synthetic and real-world datasets including images and tabular data validate the efficacy of our EvoRate.

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AAAI Conference 2023 Conference Paper

Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation

Pengcheng Xu
Boyu Wang
Charles Ling

Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets, which yields limited performance, especially under the label distribution shift. We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of classes. However, we observe that the cluster assumption in BTDA does not comprehensively hold. The hybrid categorical feature space hinders the modeling of categorical distributions and the generation of reliable pseudo labels for categorical alignment. To address these, we propose a categorical domain discriminator guided by uncertainty to explicitly model and directly align categorical distributions P(Z|Y). Simultaneously, we utilize the low-level features to augment the single source features with diverse target styles to rectify the biased classifier P(Y|Z) among diverse targets. Such a mutual conditional alignment of P(Z|Y) and P(Y|Z) forms a mutual reinforced mechanism. Our approach outperforms the state-of-the-art in BTDA even compared with methods utilizing domain labels, especially under the label distribution shift, and in single target DA on DomainNet.

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AAAI Conference 2023 Conference Paper

Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment

Qiuhao Zeng
Wei Wang
Fan Zhou
Charles Ling
Boyu Wang

Existing domain generalization aims to learn a generalizable model to perform well even on unseen domains. For many real-world machine learning applications, the data distribution often shifts gradually along domain indices. For example, a self-driving car with a vision system drives from dawn to dusk, with the sky gradually darkening. Therefore, the system must be able to adapt to changes in ambient illuminations and continue to drive safely on the road. In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment. We then propose Directional Domain Augmentation (DDA), which simulates the unseen target features by mapping source data as augmentations through a domain transformer. Specifically, we formulate DDA as a bi-level optimization problem and solve it through a novel meta-learning approach in the representation space. We evaluate the proposed method on both synthetic datasets and real-world datasets, and empirical results show that our approach can outperform other existing methods.

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NeurIPS Conference 2018 Conference Paper

Pelee: A Real-Time Object Detection System on Mobile Devices

Robert Wang
Xiang Li
Charles Ling

An increasing need of running Convolutional Neural Network (CNN) models on mobile devices with limited computing power and memory resource encourages studies on efficient model design. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and MobileNetV2. However, all these models are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead. On ImageNet ILSVRC 2012 dataset, our proposed PeleeNet achieves a higher accuracy and 1. 8 times faster speed than MobileNet and MobileNetV2 on NVIDIA TX2. Meanwhile, PeleeNet is only 66% of the model size of MobileNet. We then propose a real-time object detection system by combining PeleeNet with Single Shot MultiBox Detector (SSD) method and optimizing the architecture for fast speed. Our proposed detection system, named Pelee, achieves 76. 4% mAP (mean average precision) on PASCAL VOC2007 and 22. 4 mAP on MS COCO dataset at the speed of 23. 6 FPS on iPhone 8 and 125 FPS on NVIDIA TX2. The result on COCO outperforms YOLOv2 in consideration of a higher precision, 13. 6 times lower computational cost and 11. 3 times smaller model size. The code and models are open sourced.

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AAAI Conference 2017 Conference Paper

Fast Generalized Distillation for Semi-Supervised Domain Adaptation

Shuang Ao
Xiang Li
Charles Ling

Semi-supervised domain adaptation (SDA) is a typical setting when we face the problem of domain adaptation in real applications. How to effectively utilize the unlabeled data is an important issue in SDA. Previous work requires access to the source data to measure the data distribution mismatch, which is ineffective, when the size of the source data is relatively large. In this paper, we propose a new paradigm, called Generalized Distillation Semi-supervised Domain Adaptation (GDSDA). We show that without accessing the source data, GDSDA can effectively utilize the unlabeled data to transfer the knowledge from the source models. Then we propose GDSDA-SVM which uses SVM as the base classiﬁer and can efﬁciently solve the SDA problem. Experimental results show that GDSDA-SVM can effectively utilize the unlabeled data to transfer the knowledge between different domains under the SDA setting.

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