On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent. - Bo Han 003; Ivor W. Tsang; Ling Chen 006 (ECML/PKDD (1), 2016)

Masking: A New Perspective of Noisy Supervision. - Bo Han 003; Jiangchao Yao; Gang Niu 001; Mingyuan Zhou; Ivor W. Tsang; Ya Zhang 002; Masashi Sugiyama (NeurIPS, 2018)

Co-teaching: Robust training of deep neural networks with extremely noisy labels. - Bo Han 003; Quanming Yao; Xingrui Yu; Gang Niu 001; Miao Xu 001; Weihua Hu; Ivor W. Tsang; Masashi Sugiyama (NeurIPS, 2018)

Co-sampling: Training Robust Networks for Extremely Noisy Supervision. - Bo Han 003; Quanming Yao; Xingrui Yu; Gang Niu 001; Miao Xu 001; Weihua Hu; Ivor W. Tsang; Masashi Sugiyama (CoRR, 2018)

Matrix Co-completion for Multi-label Classification with Missing Features and Labels. - Miao Xu 001; Gang Niu 001; Bo Han 003; Ivor W. Tsang; Zhi-Hua Zhou; Masashi Sugiyama (CoRR, 2018)

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. - Bo Han 003; Gang Niu 001; Jiangchao Yao; Xingrui Yu; Miao Xu 001; Ivor W. Tsang; Masashi Sugiyama (CoRR, 2018)

Robust Plackett-Luce model for k-ary crowdsourced preferences. - Bo Han 003; Yuangang Pan; Ivor W. Tsang (Mach. Learn., 2018)

Stagewise learning for noisy k-ary preferences. - Yuangang Pan; Bo Han 003; Ivor W. Tsang (Mach. Learn., 2018)

Progressive Stochastic Learning for Noisy Labels. - Bo Han 003; Ivor W. Tsang; Ling Chen 006; Celina Ping Yu; Sai-Fu Fung (IEEE Trans. Neural Networks Learn. Syst., 2018)

VisDrone-SOT2019: The Vision Meets Drone Single Object Tracking Challenge Results. - Dawei Du; Yue Zhang 011; Liefeng Bo; Hailin Shi; Rui Zhu 005; Bo Han 003; Chunhui Zhang; Guizhong Liu; Han Wu; Hao Wen; Haoran Wang 008; Pengfei Zhu 001; et al. (ICCV Workshops, 2019)

Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. - Quanming Yao; James Tin-Yau Kwok; Bo Han 003 (ICML, 2019)

How does Disagreement Help Generalization against Label Corruption? - Xingrui Yu; Bo Han 003; Jiangchao Yao; Gang Niu 001; Ivor W. Tsang; Masashi Sugiyama (ICML, 2019)

Towards Robust ResNet: A Small Step but a Giant Leap. - Jingfeng Zhang; Bo Han 003; Laura Wynter; Bryan Kian Hsiang Low; Mohan S. Kankanhalli (IJCAI, 2019)

Are Anchor Points Really Indispensable in Label-Noise Learning? - Xiaobo Xia; Tongliang Liu; Nannan Wang 001; Bo Han 003; Chen Gong 002; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2019)

Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. - Miao Xu 001; Bingcong Li; Gang Niu 001; Bo Han 003; Masashi Sugiyama (CoRR, 2019)

Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. - Feng Liu 003; Jie Lu 001; Bo Han 003; Gang Niu 001; Guangquan Zhang 001; Masashi Sugiyama (CoRR, 2019)

VILD: Variational Imitation Learning with Diverse-quality Demonstrations. - Voot Tangkaratt; Bo Han 003; Mohammad Emtiyaz Khan; Masashi Sugiyama (CoRR, 2019)

Searching to Exploit Memorization Effect in Learning from Corrupted Labels. - Hansi Yang; Quanming Yao; Bo Han 003; Gang Niu 001 (CoRR, 2019)

Where is the Bottleneck of Adversarial Learning with Unlabeled Data? - Jingfeng Zhang; Bo Han 003; Gang Niu 001; Tongliang Liu; Masashi Sugiyama (CoRR, 2019)

Millionaire: a hint-guided approach for crowdsourcing. - Bo Han 003; Quanming Yao; Yuangang Pan; Ivor W. Tsang; Xiaokui Xiao; Qiang Yang 001; Masashi Sugiyama (Mach. Learn., 2019)

Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels. - Bo Han 003; Ivor W. Tsang; Ling Chen 006; Joey Tianyi Zhou; Celina Ping Yu (IEEE Trans. Neural Networks Learn. Syst., 2019)

Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. - Chao Li 013; Mohammad Emtiyaz Khan; Zhun Sun; Gang Niu 001; Bo Han 003; Shengli Xie; Qibin Zhao (AAAI, 2020)

Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure. - Chun Wang; Bo Han 003; Shirui Pan; Jing Jiang 002; Gang Niu 001; Guodong Long (ICDM, 2020)

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. - Bo Han 003; Gang Niu 001; Xingrui Yu; Quanming Yao; Miao Xu 001; Ivor W. Tsang; Masashi Sugiyama (ICML, 2020)

Learning with Multiple Complementary Labels. - Lei Feng 006; Takuo Kaneko; Bo Han 003; Gang Niu 001; Bo An 001; Masashi Sugiyama (ICML, 2020)

Variational Imitation Learning with Diverse-quality Demonstrations. - Voot Tangkaratt; Bo Han 003; Mohammad Emtiyaz Khan; Masashi Sugiyama (ICML, 2020)

Searching to Exploit Memorization Effect in Learning with Noisy Labels. - Quanming Yao; Hansi Yang; Bo Han 003; Gang Niu 001; James Tin-Yau Kwok (ICML, 2020)

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. - Jingfeng Zhang; Xilie Xu; Bo Han 003; Gang Niu 001; Lizhen Cui; Masashi Sugiyama; Mohan S. Kankanhalli (ICML, 2020)

A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery. - Yijing Luo; Bo Han 003; Chen Gong 002 (IJCAI, 2020)

Provably Consistent Partial-Label Learning. - Lei Feng 006; Jiaqi Lv; Bo Han 003; Miao Xu 001; Gang Niu 001; Xin Geng 001; Bo An 001; Masashi Sugiyama (NeurIPS, 2020)

Part-dependent Label Noise: Towards Instance-dependent Label Noise. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Mingming Gong; Haifeng Liu; Gang Niu 001; Dacheng Tao; Masashi Sugiyama (NeurIPS, 2020)

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Jiankang Deng; Gang Niu 001; Masashi Sugiyama (NeurIPS, 2020)

Confusable Learning for Large-Class Few-Shot Classification. - Bingcong Li; Bo Han 003; Zhuowei Wang 003; Jing Jiang 002; Guodong Long (ECML/PKDD (2), 2020)

Towards Mixture Proportion Estimation without Irreducibility. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Dacheng Tao (CoRR, 2020)

Multi-Class Classification from Noisy-Similarity-Labeled Data. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (CoRR, 2020)

Class2Simi: A New Perspective on Learning with Label Noise. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (CoRR, 2020)

Parts-dependent Label Noise: Towards Instance-dependent Label Noise. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Mingming Gong; Haifeng Liu; Gang Niu 001; Dacheng Tao; Masashi Sugiyama (CoRR, 2020)

Maximum Mean Discrepancy is Aware of Adversarial Attacks. - Ruize Gao; Feng Liu 003; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Masashi Sugiyama (CoRR, 2020)

A Survey of Label-noise Representation Learning: Past, Present and Future. - Bo Han 003; Quanming Yao; Tongliang Liu; Gang Niu 001; Ivor W. Tsang; James T. Kwok; Masashi Sugiyama (CoRR, 2020)

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Nannan Wang 001; Jiankang Deng; Jiatong Li; Yinian Mao (CoRR, 2020)

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. - Qizhou Wang; Bo Han 003; Tongliang Liu; Gang Niu 001; Jian Yang 003; Chen Gong 002 (AAAI, 2021)

Learning with Group Noise. - Qizhou Wang; Jiangchao Yao; Chen Gong 002; Tongliang Liu; Mingming Gong; Hongxia Yang; Bo Han 003 (AAAI, 2021)

HyperGraph Convolution Based Attributed HyperGraph Clustering. - Barakeel Fanseu Kamhoua; Lin Zhang; Kaili Ma 001; James Cheng; Bo Li 001; Bo Han 003 (CIKM, 2021)

Fraud Detection under Multi-Sourced Extremely Noisy Annotations. - Chuang Zhang; Qizhou Wang; Tengfei Liu; Xun Lu; Jin Hong; Bo Han 003; Chen Gong 002 (CIKM, 2021)

Robust early-learning: Hindering the memorization of noisy labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Chen Gong 002; Nannan Wang 001; Zongyuan Ge; Yi Chang (ICLR, 2021)

Geometry-aware Instance-reweighted Adversarial Training. - Jingfeng Zhang; Jianing Zhu; Gang Niu 001; Bo Han 003; Masashi Sugiyama; Mohan S. Kankanhalli (ICLR, 2021)

Confidence Scores Make Instance-dependent Label-noise Learning Possible. - Antonin Berthon; Bo Han 003; Gang Niu 001; Tongliang Liu; Masashi Sugiyama (ICML, 2021)

Learning Diverse-Structured Networks for Adversarial Robustness. - Xuefeng Du; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Yu Rong 001; Gang Niu 001; Junzhou Huang; Masashi Sugiyama (ICML, 2021)

Pointwise Binary Classification with Pairwise Confidence Comparisons. - Lei Feng 006; Senlin Shu; Nan Lu; Bo Han 003; Miao Xu 001; Gang Niu 001; Bo An 001; Masashi Sugiyama (ICML, 2021)

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. - Ruize Gao; Feng Liu 003; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Provably End-to-end Label-noise Learning without Anchor Points. - Xuefeng Li; Tongliang Liu; Bo Han 003; Gang Niu 001; Masashi Sugiyama (ICML, 2021)

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. - Songhua Wu; Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Nannan Wang 001; Haifeng Liu; Gang Niu 001 (ICML, 2021)

Towards Defending against Adversarial Examples via Attack-Invariant Features. - Dawei Zhou 004; Tongliang Liu; Bo Han 003; Nannan Wang 001; Chunlei Peng; Xinbo Gao 001 (ICML, 2021)

Device-Cloud Collaborative Learning for Recommendation. - Jiangchao Yao; Feng Wang 072; Kunyang Jia; Bo Han 003; Jingren Zhou 001; Hongxia Yang (KDD, 2021)

Understanding and Improving Early Stopping for Learning with Noisy Labels. - Yingbin Bai; Erkun Yang; Bo Han 003; Yanhua Yang; Jiatong Li; Yinian Mao; Gang Niu 001; Tongliang Liu (NeurIPS, 2021)

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. - Haoang Chi; Feng Liu 003; Wenjing Yang 002; Long Lan; Tongliang Liu; Bo Han 003; William K. Cheung 001; James T. Kwok (NeurIPS, 2021)

Universal Semi-Supervised Learning. - Zhuo Huang; Chao Xue; Bo Han 003; Jian Yang 003; Chen Gong 002 (NeurIPS, 2021)

Probabilistic Margins for Instance Reweighting in Adversarial Training. - Qizhou Wang; Feng Liu 003; Bo Han 003; Tongliang Liu; Chen Gong 002; Gang Niu 001; Mingyuan Zhou; Masashi Sugiyama (NeurIPS, 2021)

Instance-dependent Label-noise Learning under a Structural Causal Model. - Yu Yao 005; Tongliang Liu; Mingming Gong; Bo Han 003; Gang Niu 001; Kun Zhang 001 (NeurIPS, 2021)

Understanding the Interaction of Adversarial Training with Noisy Labels. - Jianing Zhu; Jingfeng Zhang; Bo Han 003; Tongliang Liu; Gang Niu 001; Hongxia Yang; Mohan S. Kankanhalli; Masashi Sugiyama (CoRR, 2021)

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. - Haoang Chi; Feng Liu 003; Wenjing Yang 002; Long Lan; Tongliang Liu; Gang Niu 001; Bo Han 003 (CoRR, 2021)

Estimating Instance-dependent Label-noise Transition Matrix using DNNs. - Shuo Yang 006; Erkun Yang; Bo Han 003; Yang Liu 018; Min Xu 001; Gang Niu 001; Tongliang Liu (CoRR, 2021)

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? - Jingfeng Zhang; Xilie Xu; Bo Han 003; Tongliang Liu; Gang Niu 001; Lizhen Cui; Masashi Sugiyama (CoRR, 2021)

Instance Correction for Learning with Open-set Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2021)

Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training. - Dawei Zhou 004; Nannan Wang 001; Xinbo Gao 001; Bo Han 003; Jun Yu 001; Xiaoyu Wang 002; Tongliang Liu (CoRR, 2021)

KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation. - Chenhong Zhou; Feng Liu 003; Chen Gong 002; Tongliang Liu; Bo Han 003; William Kwok-Wai Cheung (CoRR, 2021)

PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels. - Xuefeng Du; Tian Bian; Yu Rong 001; Bo Han 003; Tongliang Liu; Tingyang Xu; Wenbing Huang 001; Junzhou Huang (CoRR, 2021)

Federated Noisy Client Learning. - Li Li 064; Huazhu Fu; Bo Han 003; Cheng-Zhong Xu 001; Ling Shao 001 (CoRR, 2021)

Local Reweighting for Adversarial Training. - Ruize Gao; Feng Liu 003; Kaiwen Zhou; Gang Niu 001; Bo Han 003; James Cheng (CoRR, 2021)

Modelling Adversarial Noise for Adversarial Defense. - Dawei Zhou 004; Nannan Wang 001; Tongliang Liu; Bo Han 003 (CoRR, 2021)

MC$^2$-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation. - Zeyuan Chen; Jiangchao Yao; Feng Wang 072; Kunyang Jia; Bo Han 003; Wei Zhang; Hongxia Yang (CoRR, 2021)

Click-through Rate Prediction with Auto-Quantized Contrastive Learning. - Yujie Pan 001; Jiangchao Yao; Bo Han 003; Kunyang Jia; Ya Zhang 002; Hongxia Yang (CoRR, 2021)

Privacy-Preserving Stochastic Gradual Learning. - Bo Han 003; Ivor W. Tsang; Xiaokui Xiao; Ling Chen 006; Sai-Fu Fung; Celina Ping Yu (IEEE Trans. Knowl. Data Eng., 2021)

Learning and Mining with Noisy Labels. - Masashi Sugiyama; Tongliang Liu; Bo Han 003; Yang Liu 018; Gang Niu 001 (CIKM, 2022)

Fair Classification with Instance-dependent Label Noise. - Songhua Wu; Mingming Gong; Bo Han 003; Yang Liu 018; Tongliang Liu (CLeaR, 2022)

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. - De Cheng; Tongliang Liu; Yixiong Ning; Nannan Wang 001; Bo Han 003; Gang Niu 001; Xinbo Gao 001; Masashi Sugiyama (CVPR, 2022)

EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs. - Guohao Ying; Xin He; Bin Gao; Bo Han 003; Xiaowen Chu 001 (ECCV (16), 2022)

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. - Yongqiang Chen 002; Han Yang 002; Yonggang Zhang 003; Kaili Ma 001; Tongliang Liu; Bo Han 003; James Cheng (ICLR, 2022)

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. - Haoang Chi; Feng Liu 003; Wenjing Yang 002; Long Lan; Tongliang Liu; Bo Han 003; Gang Niu 001; Mingyuan Zhou; Masashi Sugiyama (ICLR, 2022)

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. - Xiaobo Xia; Tongliang Liu; Bo Han 003; Mingming Gong; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (ICLR, 2022)

Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. - Yu Yao 005; Tongliang Liu; Bo Han 003; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Dacheng Tao (ICLR, 2022)

Exploiting Class Activation Value for Partial-Label Learning. - Fei Zhang; Lei Feng 006; Bo Han 003; Tongliang Liu; Gang Niu 001; Tao Qin; Masashi Sugiyama (ICLR, 2022)

Adversarial Robustness Through the Lens of Causality. - Yonggang Zhang 003; Mingming Gong; Tongliang Liu; Gang Niu 001; Xinmei Tian 001; Bo Han 003; Bernhard Schölkopf; Kun Zhang 001 (ICLR, 2022)

Reliable Adversarial Distillation with Unreliable Teachers. - Jianing Zhu; Jiangchao Yao; Bo Han 003; Jingfeng Zhang; Tongliang Liu; Gang Niu 001; Jingren Zhou 001; Jianliang Xu; Hongxia Yang (ICLR, 2022)

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. - Ruize Gao; Jiongxiao Wang; Kaiwen Zhou; Feng Liu 003; Binghui Xie; Gang Niu 001; Bo Han 003; James Cheng (ICML, 2022)

Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. - Zhenheng Tang; Yonggang Zhang 003; Shaohuai Shi; Xin He; Bo Han 003; Xiaowen Chu 001 (ICML, 2022)

Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. - Shuo Yang 006; Erkun Yang; Bo Han 003; Yang Liu 018; Min Xu 001; Gang Niu 001; Tongliang Liu (ICML, 2022)

Understanding Robust Overfitting of Adversarial Training and Beyond. - Chaojian Yu; Bo Han 003; Li Shen 008; Jun Yu 001; Chen Gong 002; Mingming Gong; Tongliang Liu (ICML, 2022)

Improving Adversarial Robustness via Mutual Information Estimation. - Dawei Zhou 004; Nannan Wang 001; Xinbo Gao 001; Bo Han 003; Xiaoyu Wang 002; Yibing Zhan; Tongliang Liu (ICML, 2022)

Modeling Adversarial Noise for Adversarial Training. - Dawei Zhou 004; Nannan Wang 001; Bo Han 003; Tongliang Liu (ICML, 2022)

Contrastive Learning with Boosted Memorization. - Zhihan Zhou 002; Jiangchao Yao; Yanfeng Wang 001; Bo Han 003; Ya Zhang 002 (ICML, 2022)

Robust Weight Perturbation for Adversarial Training. - Chaojian Yu; Bo Han 003; Mingming Gong; Li Shen 008; Shiming Ge; Du Bo; Tongliang Liu (IJCAI, 2022)

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. - Xiong Peng; Feng Liu 003; Jingfeng Zhang; Long Lan; Junjie Ye; Tongliang Liu; Bo Han 003 (KDD, 2022)

Device-cloud Collaborative Recommendation via Meta Controller. - Jiangchao Yao; Feng Wang 072; Xichen Ding; Shaohu Chen; Bo Han 003; Jingren Zhou 001; Hongxia Yang (KDD, 2022)

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. - Yongqiang Chen 002; Yonggang Zhang 003; Yatao Bian; Han Yang 002; Kaili Ma 001; Binghui Xie; Tongliang Liu; Bo Han 003; James Cheng (NeurIPS, 2022)

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. - Jianan Zhou 002; Jianing Zhu; Jingfeng Zhang; Tongliang Liu; Gang Niu 001; Bo Han 003; Masashi Sugiyama (NeurIPS, 2022)

RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning. - Yingbin Bai; Erkun Yang; Zhaoqing Wang; Yuxuan Du; Bo Han 003; Cheng Deng; Dadong Wang; Tongliang Liu (NeurIPS, 2022)

Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization. - De Cheng; Yixiong Ning; Nannan Wang 001; Xinbo Gao 001; Heng Yang; Yuxuan Du; Bo Han 003; Tongliang Liu (NeurIPS, 2022)

Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. - Sen Cui; Jingfeng Zhang; Jian Liang 002; Bo Han 003; Masashi Sugiyama; Changshui Zhang (NeurIPS, 2022)

Is Out-of-Distribution Detection Learnable? - Zhen Fang 001; Yixuan Li 001; Jie Lu 001; Jiahua Dong 001; Bo Han 003; Feng Liu 003 (NeurIPS, 2022)

Exact Shape Correspondence via 2D graph convolution. - Barakeel Fanseu Kamhoua; Lin Zhang; Yongqiang Chen 002; Han Yang 002; Kaili Ma 001; Bo Han 003; Bo Li 001; James Cheng (NeurIPS, 2022)

Towards Lightweight Black-Box Attack Against Deep Neural Networks. - Chenghao Sun; Yonggang Zhang 003; Chaoqun Wan; Qizhou Wang; Ya Li; Tongliang Liu; Bo Han 003; Xinmei Tian 001 (NeurIPS, 2022)

Watermarking for Out-of-distribution Detection. - Qizhou Wang; Feng Liu 003; Yonggang Zhang 003; Jing Zhang 037; Chen Gong 002; Tongliang Liu; Bo Han 003 (NeurIPS, 2022)

Pluralistic Image Completion with Gaussian Mixture Models. - Xiaobo Xia; Wenhao Yang; Jie Ren; Yewen Li; Yibing Zhan; Bo Han 003; Tongliang Liu (NeurIPS, 2022)

Counterfactual Fairness with Partially Known Causal Graph. - Aoqi Zuo; Susan Wei; Tongliang Liu; Bo Han 003; Kun Zhang 001; Mingming Gong (NeurIPS, 2022)

Do We Need to Penalize Variance of Losses for Learning with Label Noise? - Yexiong Lin; Yu Yao 005; Yuxuan Du; Jun Yu 001; Bo Han 003; Mingming Gong; Tongliang Liu (CoRR, 2022)

Invariance Principle Meets Out-of-Distribution Generalization on Graphs. - Yongqiang Chen 002; Yonggang Zhang 003; Han Yang 002; Kaili Ma 001; Binghui Xie; Tongliang Liu; Bo Han 003; James Cheng (CoRR, 2022)

Pluralistic Image Completion with Probabilistic Mixture-of-Experts. - Xiaobo Xia; Wenhao Yang; Jie Ren; Yewen Li; Yibing Zhan; Bo Han 003; Tongliang Liu (CoRR, 2022)

FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels. - Zhuowei Wang 003; Tianyi Zhou 001; Guodong Long; Bo Han 003; Jing Jiang 002 (CoRR, 2022)

Robust Weight Perturbation for Adversarial Training. - Chaojian Yu; Bo Han 003; Mingming Gong; Li Shen 008; Shiming Ge; Bo Du 001; Tongliang Liu (CoRR, 2022)

Learning Adaptive Propagation for Knowledge Graph Reasoning. - Yongqi Zhang; Zhanke Zhou; Quanming Yao; Xiaowen Chu 001; Bo Han 003 (CoRR, 2022)

MSR: Making Self-supervised learning Robust to Aggressive Augmentations. - Yingbin Bai; Erkun Yang; Zhaoqing Wang; Yuxuan Du; Bo Han 003; Cheng Deng; Dadong Wang; Tongliang Liu (CoRR, 2022)

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. - Xiong Peng; Feng Liu 003; Jingfen Zhang; Long Lan; Junjie Ye; Tongliang Liu; Bo Han 003 (CoRR, 2022)

Pareto Invariant Risk Minimization. - Yongqiang Chen 002; Kaiwen Zhou; Yatao Bian; Binghui Xie; Kaili Ma 001; Yonggang Zhang 003; Han Yang 002; Bo Han 003; James Cheng (CoRR, 2022)

Efficient Private SCO for Heavy-Tailed Data via Clipping. - Chenhan Jin; Kaiwen Zhou; Bo Han 003; James Cheng; Ming-Chang Yang (CoRR, 2022)

Combating Noisy-Labeled and Imbalanced Data by Two Stage Bi-Dimensional Sample Selection. - Yiliang Zhang; Yang Lu 009; Bo Han 003; Yiu-ming Cheung; Hanzi Wang (CoRR, 2022)

Towards Lightweight Black-Box Attacks against Deep Neural Networks. - Chenghao Sun; Yonggang Zhang 003; Chaoqun Wan; Qizhou Wang; Ya Li; Tongliang Liu; Bo Han 003; Xinmei Tian 001 (CoRR, 2022)

Strength-Adaptive Adversarial Training. - Chaojian Yu; Dawei Zhou 004; Li Shen 008; Jun Yu 001; Bo Han 003; Mingming Gong; Nannan Wang 001; Tongliang Liu (CoRR, 2022)

Learning from Noisy Pairwise Similarity and Unlabeled Data. - Songhua Wu; Tongliang Liu; Bo Han 003; Jun Yu 001; Gang Niu 001; Masashi Sugiyama (J. Mach. Learn. Res., 2022)

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. - Quanming Yao; Yaqing Wang 002; Bo Han 003; James T. Kwok (J. Mach. Learn. Res., 2022)

Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation. - Chen Gong 002; Qizhou Wang; Tongliang Liu; Bo Han 003; Jane You; Jian Yang 003; Dacheng Tao (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. - Zhuowei Wang 003; Jing Jiang 002; Bo Han 003; Lei Feng 006; Bo An 001; Gang Niu 001; Guodong Long (Trans. Mach. Learn. Res., 2022)

NoiLin: Improving adversarial training and correcting stereotype of noisy labels. - Jingfeng Zhang; Xilie Xu; Bo Han 003; Tongliang Liu; Lizhen Cui; Gang Niu 001; Masashi Sugiyama (Trans. Mach. Learn. Res., 2022)

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension. - Xin He; Jiangchao Yao; Yuxin Wang 003; Zhenheng Tang; Ka Chun Cheung; Simon See; Bo Han 003; Xiaowen Chu 001 (AAAI, 2023)

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. - Zhuo Huang; Miaoxi Zhu; Xiaobo Xia; Li Shen 008; Jun Yu 001; Chen Gong 002; Bo Han 003; Bo Du 001; Tongliang Liu (CVPR, 2023)

Hard Sample Matters a Lot in Zero-Shot Quantization. - Huantong Li; Xiangmiao Wu; Fanbing Lv; Daihai Liao; Thomas H. Li; Yonggang Zhang 003; Bo Han 003; Mingkui Tan (CVPR, 2023)

Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis. - Ke Liu 013; Feng Liu 003; Haishuai Wang; Ning Ma; Jiajun Bu; Bo Han 003 (ICCV, 2023)

Label-Noise Learning with Intrinsically Long-Tailed Data. - Yang Lu 009; Yiliang Zhang; Bo Han 003; Yiu-Ming Cheung; Hanzi Wang (ICCV, 2023)

Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples. - Xiaobo Xia; Bo Han 003; Yibing Zhan; Jun Yu 001; Mingming Gong; Chen Gong 002; Tongliang Liu (ICCV, 2023)

Holistic Label Correction for Noisy Multi-Label Classification. - Xiaobo Xia; Jiankang Deng; Wei Bao; Yuxuan Du; Bo Han 003; Shiguang Shan; Tongliang Liu (ICCV, 2023)

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. - Yongqiang Chen 002; Kaiwen Zhou; Yatao Bian; Binghui Xie; Bingzhe Wu; Yonggang Zhang 003; Kaili Ma 001; Han Yang 002; Peilin Zhao; Bo Han 003; James Cheng (ICLR, 2023)

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. - Zhuo Huang; Xiaobo Xia; Li Shen 008; Bo Han 003; Mingming Gong; Chen Gong 002; Tongliang Liu (ICLR, 2023)

A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond. - Yong Lin; Renjie Pi; Weizhong Zhang; Xiaobo Xia; Jiahui Gao; Xiao Zhou; Tongliang Liu; Bo Han 003 (ICLR, 2023)

Out-of-distribution Detection with Implicit Outlier Transformation. - Qizhou Wang; Junjie Ye; Feng Liu 003; Quanyu Dai; Marcus Kalander; Tongliang Liu; Jianye Hao; Bo Han 003 (ICLR, 2023)

Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning. - Xiaobo Xia; Jiale Liu; Jun Yu 001; Xu Shen 001; Bo Han 003; Tongliang Liu (ICLR, 2023)

Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. - Jianing Zhu; Jiangchao Yao; Tongliang Liu; Quanming Yao; Jianliang Xu; Bo Han 003 (ICLR, 2023)

Moderately Distributional Exploration for Domain Generalization. - Rui Dai; Yonggang Zhang 003; Zhen Fang 001; Bo Han 003; Xinmei Tian 001 (ICML, 2023)

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. - Ruijiang Dong; Feng Liu 003; Haoang Chi; Tongliang Liu; Mingming Gong; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (ICML, 2023)

Detecting Out-of-distribution Data through In-distribution Class Prior. - Xue Jiang; Feng Liu 003; Zhen Fang 001; Hong Chen 004; Tongliang Liu; Feng Zheng; Bo Han 003 (ICML, 2023)

A Universal Unbiased Method for Classification from Aggregate Observations. - Zixi Wei; Lei Feng 006; Bo Han 003; Tongliang Liu; Gang Niu 001; Xiaofeng Zhu 001; Heng Tao Shen (ICML, 2023)

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? - Yu Yao 005; Mingming Gong; Yuxuan Du; Jun Yu 001; Bo Han 003; Kun Zhang 001; Tongliang Liu (ICML, 2023)

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score. - Shuhai Zhang; Feng Liu 003; Jiahao Yang; Yifan Yang; Changsheng Li; Bo Han 003; Mingkui Tan (ICML, 2023)

On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation. - Zhanke Zhou; Chenyu Zhou; Xuan Li 005; Jiangchao Yao; Quanming Yao; Bo Han 003 (ICML, 2023)

Exploring Model Dynamics for Accumulative Poisoning Discovery. - Jianing Zhu; Xiawei Guo; Jiangchao Yao; Chao Du; Li He; Shuo Yuan; Tongliang Liu; Liang Wang 001; Bo Han 003 (ICML, 2023)

Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability. - Jianing Zhu; Hengzhuang Li; Jiangchao Yao; Tongliang Liu; Jianliang Xu; Bo Han 003 (ICML, 2023)

AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning. - Yongqi Zhang; Zhanke Zhou; Quanming Yao; Xiaowen Chu 001; Bo Han 003 (KDD, 2023)

Combating Representation Learning Disparity with Geometric Harmonization. - Zhihan Zhou 002; Jiangchao Yao; Feng Hong 004; Ya Zhang 002; Bo Han 003; Yanfeng Wang 001 (NeurIPS, 2023)

Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping. - Yingbin Bai; Zhongyi Han; Erkun Yang; Jun Yu 001; Bo Han 003; Dadong Wang; Tongliang Liu (NeurIPS, 2023)

Does Invariant Graph Learning via Environment Augmentation Learn Invariance? - Yongqiang Chen 002; Yatao Bian; Kaiwen Zhou; Binghui Xie; Bo Han 003; James Cheng (NeurIPS, 2023)

Understanding and Improving Feature Learning for Out-of-Distribution Generalization. - Yongqiang Chen 002; Wei Huang; Kaiwen Zhou; Yatao Bian; Bo Han 003; James Cheng (NeurIPS, 2023)

Federated Learning with Bilateral Curation for Partially Class-Disjoint Data. - Ziqing Fan; Ruipeng Zhang; Jiangchao Yao; Bo Han 003; Ya Zhang 002; Yanfeng Wang 001 (NeurIPS, 2023)

FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning. - Zhuo Huang; Li Shen 008; Jun Yu 001; Bo Han 003; Tongliang Liu (NeurIPS, 2023)

InstanT: Semi-supervised Learning with Instance-dependent Thresholds. - Muyang Li; Runze Wu; Haoyu Liu; Jun Yu 001; Xun Yang 001; Bo Han 003; Tongliang Liu (NeurIPS, 2023)

Learning to Augment Distributions for Out-of-distribution Detection. - Qizhou Wang; Zhen Fang 001; Yonggang Zhang 003; Feng Liu 003; Yixuan Li 001; Bo Han 003 (NeurIPS, 2023)

SODA: Robust Training of Test-Time Data Adaptors. - Zige Wang; Yonggang Zhang 003; Zhen Fang 001; Long Lan; Wenjing Yang 002; Bo Han 003 (NeurIPS, 2023)

FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. - Zhiqin Yang; Yonggang Zhang 003; Yu Zheng 021; Xinmei Tian 001; Hao Peng; Tongliang Liu; Bo Han 003 (NeurIPS, 2023)

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. - Haotian Zheng; Qizhou Wang; Zhen Fang 001; Xiaobo Xia; Feng Liu 003; Tongliang Liu; Bo Han 003 (NeurIPS, 2023)

Combating Bilateral Edge Noise for Robust Link Prediction. - Zhanke Zhou; Jiangchao Yao; Jiaxu Liu; Xiawei Guo; Quanming Yao; Li He; Liang Wang 001; Bo Zheng 007; Bo Han 003 (NeurIPS, 2023)

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. - Jianing Zhu; Yu Geng; Jiangchao Yao; Tongliang Liu; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (NeurIPS, 2023)

Federated Semi-Supervised Learning with Annotation Heterogeneity. - Xinyi Shang; Gang Huang 004; Yang Lu 009; Jian Lou 001; Bo Han 003; Yiu-ming Cheung; Hanzi Wang (CoRR, 2023)

Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. - Jiren Mai; Fei Zhang; Junjie Ye; Marcus Kalander; Xian Zhang; Wankou Yang; Tongliang Liu; Bo Han 003 (CoRR, 2023)

Towards Understanding Feature Learning in Out-of-Distribution Generalization. - Yongqiang Chen 002; Wei Huang; Kaiwen Zhou; Yatao Bian; Bo Han 003; James Cheng (CoRR, 2023)

Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. - Jingfeng Zhang; Bo Song; Bo Han 003; Lei Liu 003; Gang Niu 001; Masashi Sugiyama (CoRR, 2023)

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. - Yuhao Wu; Xiaobo Xia; Jun Yu 001; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Tongliang Liu (CoRR, 2023)

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels. - Hui Kang; Sheng Liu; Huaxi Huang; Jun Yu 001; Bo Han 003; Dadong Wang; Tongliang Liu (CoRR, 2023)

MCM: Multi-condition Motion Synthesis Framework for Multi-scenario. - Zeyu Ling; Bo Han 003; Yongkang Wong; Mohan S. Kankanhalli; Weidong Geng (CoRR, 2023)

On the Onset of Robust Overfitting in Adversarial Training. - Chaojian Yu; Xiaolong Shi; Jun Yu 001; Bo Han 003; Tongliang Liu (CoRR, 2023)

Towards out-of-distribution generalizable predictions of chemical kinetics properties. - Zihao Wang 001; Yongqiang Chen 002; Yang Duan; Weijiang Li; Bo Han 003; James Cheng; Hanghang Tong (CoRR, 2023)

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. - Jianing Zhu; Geng Yu; Jiangchao Yao; Tongliang Liu; Gang Niu 001; Masashi Sugiyama; Bo Han 003 (CoRR, 2023)

Long-Range Neural Atom Learning for Molecular Graphs. - Xuan Li 005; Zhanke Zhou; Jiangchao Yao; Yu Rong 001; Lu Zhang 060; Bo Han 003 (CoRR, 2023)

DeepInception: Hypnotize Large Language Model to Be Jailbreaker. - Xuan Li 005; Zhanke Zhou; Jianing Zhu; Jiangchao Yao; Tongliang Liu; Bo Han 003 (CoRR, 2023)

Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes. - Yongqiang Chen 002; Binghui Xie; Kaiwen Zhou; Bo Han 003; Yatao Bian; James Cheng (CoRR, 2023)

Are All Unseen Data Out-of-Distribution? - Songming Zhang 002; Yuxiao Luo 001; Qizhou Wang; Haoang Chi; Weikai Li 003; Bo Han 003; Jinyan Li (CoRR, 2023)

Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions. - Xuefeng Du; Tian Bian; Yu Rong 001; Bo Han 003; Tongliang Liu; Tingyang Xu; Wenbing Huang 001; Yixuan Li 001; Junzhou Huang (Trans. Mach. Learn. Res., 2023)

KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. - Chenhong Zhou; Feng Liu 003; Chen Gong 002; Rongfei Zeng; Tongliang Liu; Kwok-Wai Cheung 001; Bo Han 003 (Trans. Mach. Learn. Res., 2023)

Class-Wise Denoising for Robust Learning Under Label Noise. - Chen Gong 002; Yongliang Ding; Bo Han 003; Gang Niu 001; Jian Yang 003; Jane You; Dacheng Tao; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. - Xiaobo Xia; Bo Han 003; Nannan Wang 001; Jiankang Deng; Jiatong Li; Yinian Mao; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

GRACE: A General Graph Convolution Framework for Attributed Graph Clustering. - Barakeel Fanseu Kamhoua; Lin Zhang; Kaili Ma 001; James Cheng; Bo Li 001; Bo Han 003 (ACM Trans. Knowl. Discov. Data, 2023)

Latent Class-Conditional Noise Model. - Jiangchao Yao; Bo Han 003; Zhihan Zhou 002; Ya Zhang 002; Ivor W. Tsang (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

A Parametrical Model for Instance-Dependent Label Noise. - Shuo Yang 006; Songhua Wu; Erkun Yang; Bo Han 003; Yang Liu 018; Min Xu 001; Gang Niu 001; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Deep Learning From Multiple Noisy Annotators as A Union. - Hongxin Wei; Renchunzi Xie; Lei Feng 006; Bo Han 003; Bo An 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

Federated Learning with Extremely Noisy Clients via Negative Distillation. - Yang Lu 009; Lin Chen; Yonggang Zhang 003; Yiliang Zhang; Bo Han 003; Yiu-ming Cheung; Hanzi Wang (AAAI, 2024)

Enhancing Evolving Domain Generalization through Dynamic Latent Representations. - Binghui Xie; Yongqiang Chen 002; Jiaqi Wang 012; Kaiwen Zhou; Bo Han 003; Wei Meng 001; James Cheng (AAAI, 2024)

Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning. - Zihua Zhao; Mengxi Chen; Tianjie Dai; Jiangchao Yao; Bo Han 003; Ya Zhang 002; Yanfeng Wang 001 (CVPR, 2024)

Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. - Rong Dai; Yonggang Zhang 003; Ang Li; Tongliang Liu; Xun Yang 001; Bo Han 003 (ICLR, 2024)

Negative Label Guided OOD Detection with Pretrained Vision-Language Models. - Xue Jiang; Feng Liu 003; Zhen Fang 001; Hong Chen 004; Tongliang Liu; Feng Zheng; Bo Han 003 (ICLR, 2024)

Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel. - Xuan Li 005; Zhanke Zhou; Jiangchao Yao; Yu Rong 001; Lu Zhang 060; Bo Han 003 (ICLR, 2024)

On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. - Runqi Lin; Chaojian Yu; Bo Han 003; Tongliang Liu (ICLR, 2024)

Out-of-Distribution Detection with Negative Prompts. - Jun Nie; Yonggang Zhang 003; Zhen Fang 001; Tongliang Liu; Bo Han 003; Xinmei Tian 001 (ICLR, 2024)

FedImpro: Measuring and Improving Client Update in Federated Learning. - Zhenheng Tang; Yonggang Zhang 003; Shaohuai Shi; Xinmei Tian 001; Tongliang Liu; Bo Han 003; Xiaowen Chu 001 (ICLR, 2024)

Accurate Forgetting for Heterogeneous Federated Continual Learning. - Abudukelimu Wuerkaixi; Sen Cui; Jingfeng Zhang; Kunda Yan; Bo Han 003; Gang Niu 001; Lei Fang; Changshui Zhang; Masashi Sugiyama (ICLR, 2024)

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. - Binghui Xie; Yatao Bian; Kaiwen Zhou; Yongqiang Chen 002; Peilin Zhao; Bo Han 003; Wei Meng 001; James Cheng (ICLR, 2024)

Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy. - Shuhai Zhang; Yiliao Song; Jiahao Yang; Yuanqing Li 001; Bo Han 003; Mingkui Tan (ICLR, 2024)

Robust Training of Federated Models with Extremely Label Deficiency. - Yonggang Zhang 003; Zhiqin Yang; Xinmei Tian 001; Nannan Wang 001; Tongliang Liu; Bo Han 003 (ICLR, 2024)

Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation. - Jiyang Zheng; Yu Yao 005; Bo Han 003; Dadong Wang; Tongliang Liu (ICLR, 2024)

NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. - Pengfei Zheng; Yonggang Zhang 003; Zhen Fang 001; Tongliang Liu; Defu Lian; Bo Han 003 (ICLR, 2024)

Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs. - Zhanke Zhou; Yongqi Zhang; Jiangchao Yao; Quanming Yao; Bo Han 003 (ICLR, 2024)

How Interpretable Are Interpretable Graph Neural Networks? - Yongqiang Chen 002; Yatao Bian; Bo Han 003; James Cheng (ICML, 2024)

Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection. - Chentao Cao; Zhun Zhong; Zhanke Zhou; Yang Liu 018; Tongliang Liu; Bo Han 003 (ICML, 2024)

Towards Realistic Model Selection for Semi-supervised Learning. - Muyang Li; Xiaobo Xia; Runze Wu; Fengming Huang; Jun Yu 001; Bo Han 003; Tongliang Liu (ICML, 2024)

Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency. - Runqi Lin; Chaojian Yu; Bo Han 003; Hang Su 006; Tongliang Liu (ICML, 2024)

MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. - Hongduan Tian; Feng Liu 003; Tongliang Liu; Bo Du 001; Yiu-ming Cheung; Bo Han 003 (ICML, 2024)

Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. - Yuhao Wu; Jiangchao Yao; Bo Han 003; Lina Yao 001; Tongliang Liu (ICML, 2024)

Mitigating Label Noise on Graphs via Topological Sample Selection. - Yuhao Wu; Jiangchao Yao; Xiaobo Xia; Jun Yu 001; Ruxin Wang 002; Bo Han 003; Tongliang Liu (ICML, 2024)

Balancing Similarity and Complementarity for Federated Learning. - Kunda Yan; Sen Cui; Abudukelimu Wuerkaixi; Jingfeng Zhang; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Changshui Zhang (ICML, 2024)

Trustworthy Machine Learning under Imperfect Data. - Bo Han 003 (IJCAI, 2024)

MCM: Multi-condition Motion Synthesis Framework. - Zeyu Ling; Bo Han 003; Yongkang Wong; Han Lin; Mohan S. Kankanhalli; Weidong Geng (IJCAI, 2024)

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? - Haoang Chi; He Li; Wenjing Yang 002; Feng Liu 003; Long Lan; Xiaoguang Ren; Tongliang Liu; Bo Han 003 (NeurIPS, 2024)

Discovery of the Hidden World with Large Language Models. - Chenxi Liu; Yongqiang Chen 002; Tongliang Liu; Mingming Gong; James Cheng; Bo Han 003; Kun Zhang 001 (NeurIPS, 2024)

Revive Re-weighting in Imbalanced Learning by Density Ratio Estimation. - Jiaan Luo; Feng Hong 004; Jiangchao Yao; Bo Han 003; Ya Zhang 002; Yanfeng Wang 001 (NeurIPS, 2024)

Pseudo-Private Data Guided Model Inversion Attacks. - Xiong Peng; Bo Han 003; Feng Liu 003; Tongliang Liu; Mingyuan Zhou (NeurIPS, 2024)

FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. - Zhenheng Tang; Yonggang Zhang 003; Peijie Dong; Yiu-ming Cheung; Amelie Chi Zhou; Bo Han 003; Xiaowen Chu 001 (NeurIPS, 2024)

Mind the Gap Between Prototypes and Images in Cross-domain Finetuning. - Hongduan Tian; Feng Liu 003; Zhanke Zhou; Tongliang Liu; Chengqi Zhang; Bo Han 003 (NeurIPS, 2024)

A Sober Look at the Robustness of CLIPs to Spurious Features. - Qizhou Wang; Yong Lin; Yongqiang Chen 002; Ludwig Schmidt; Bo Han 003; Tong Zhang 001 (NeurIPS, 2024)

Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection. - Geng Yu; Jianing Zhu; Jiangchao Yao; Bo Han 003 (NeurIPS, 2024)

What If the Input is Expanded in OOD Detection? - Boxuan Zhang 001; Jianing Zhu; Zengmao Wang; Tongliang Liu; Bo Du 001; Bo Han 003 (NeurIPS, 2024)

Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales? - Zhanke Zhou; Rong Tao; Jianing Zhu; Yiwen Luo; Zengmao Wang; Bo Han 003 (NeurIPS, 2024)

Few-Shot Adversarial Prompt Learning on Vision-Language Models. - Yiwei Zhou; Xiaobo Xia; Zhiwei Lin; Bo Han 003; Tongliang Liu (NeurIPS, 2024)

Mitigating Label Noise on Graph via Topological Sample Selection. - Yuhao Wu; Jiangchao Yao; Xiaobo Xia; Jun Yu 001; Ruxin Wang 002; Bo Han 003; Tongliang Liu (CoRR, 2024)

Do CLIPs Always Generalize Better than ImageNet Models? - Qizhou Wang; Yong Lin; Yongqiang Chen 002; Ludwig Schmidt; Bo Han 003; Tong Zhang 001 (CoRR, 2024)

Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation. - Yikun Zhang; Geyan Ye; Chaohao Yuan; Bo Han 003; Long-Kai Huang; Jianhua Yao 001; Wei Liu 005; Yu Rong 001 (CoRR, 2024)

Decoupling the Class Label and the Target Concept in Machine Unlearning. - Jianing Zhu; Bo Han 003; Jiangchao Yao; Jianliang Xu; Gang Niu 001; Masashi Sugiyama (CoRR, 2024)

Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. - Qizhou Wang; Bo Han 003; Puning Yang; Jianing Zhu; Tongliang Liu; Masashi Sugiyama (CoRR, 2024)

Rethinking Improved Privacy-Utility Trade-off with Pre-existing Knowledge for DP Training. - Yu Zheng 021; Wenchao Zhang 001; Yonggang Zhang 003; Wei Song; Kai Zhou 001; Bo Han 003 (CoRR, 2024)

Knowledge-Enhanced Facial Expression Recognition with Emotional-to-Neutral Transformation. - Hangyu Li 001; Yihan Xu; Jiangchao Yao; Nannan Wang 001; Xinbo Gao 001; Bo Han 003 (CoRR, 2024)

Model Inversion Attacks: A Survey of Approaches and Countermeasures. - Zhanke Zhou; Jianing Zhu; Fengfei Yu; Xuan Li 005; Xiong Peng; Tongliang Liu; Bo Han 003 (CoRR, 2024)

Detecting Discrepancies Between AI-Generated and Natural Images Using Uncertainty. - Jun Nie; Yonggang Zhang 003; Tongliang Liu; Yiu-ming Cheung; Bo Han 003; Xinmei Tian 001 (CoRR, 2024)

Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds. - Shuhai Zhang; Jiahao Yang; Hui Luo 002; Jie Chen; Li Wang 033; Feng Liu 003; Bo Han 003; Mingkui Tan (CoRR, 2024)

USN: A Robust Imitation Learning Method against Diverse Action Noise. - Xingrui Yu; Bo Han 003; Ivor W. Tsang (J. Artif. Intell. Res., 2024)

On the Learnability of Out-of-distribution Detection. - Zhen Fang 001; Yixuan Li 001; Feng Liu 003; Bo Han 003; Jie Lu 001 (J. Mach. Learn. Res., 2024)

A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels. - Songhua Wu; Tianyi Zhou 001; Yuxuan Du; Jun Yu 001; Bo Han 003; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. - Wankou Yang; Jiren Mai; Fei Zhang; Tongliang Liu; Bo Han 003 (Trans. Mach. Learn. Res., 2024)

Understanding Fairness Surrogate Functions in Algorithmic Fairness. - Wei Yao 017; Zhanke Zhou; Zhicong Li; Bo Han 003; Yong Liu (Trans. Mach. Learn. Res., 2024)

HiFE: Hierarchical Feature Ensemble Framework for Few-shot Hypotheses Adaptation. - Yongfeng Zhong; Haoang Chi; Feng Liu 003; Xiao-Ming Wu; Bo Han 003 (Trans. Mach. Learn. Res., 2024)

Class-Wise Contrastive Prototype Learning for Semi-Supervised Classification Under Intersectional Class Mismatch. - Mingyu Li; Tao Zhou 002; Bo Han 003; Tongliang Liu; Xinkai Liang; Jiajia Zhao; Chen Gong 002 (IEEE Trans. Multim., 2024)

Learning a robust foundation model against clean-label data poisoning attacks at downstream tasks. - Ting Zhou; Hanshu Yan; Bo Han 003; Lei Liu 003; Jingfeng Zhang (Neural Networks, 2024)

Server-Client Collaborative Distillation for Federated Reinforcement Learning. - Weiming Mai; Jiangchao Yao; Chen Gong 002; Ya Zhang 002; Yiu-Ming Cheung; Bo Han 003 (ACM Trans. Knowl. Discov. Data, 2024)

Regularly Truncated M-Estimators for Learning With Noisy Labels. - Xiaobo Xia; Pengqian Lu; Chen Gong 002; Bo Han 003; Jun Yu 001; Jun Yu 002; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Online binary classification from similar and dissimilar data. - Senlin Shu; Haobo Wang 001; Zhuowei Wang 003; Bo Han 003; Tao Xiang 001; Bo An 001; Lei Feng 006 (Mach. Learn., 2024)

Exploiting counter-examples for active learning with partial labels. - Fei Zhang; Yunjie Ye; Lei Feng 006; Zhongwen Rao; Jieming Zhu; Marcus Kalander; Chen Gong 002; Jianye Hao; Bo Han 003 (Mach. Learn., 2024)

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. - Jingfeng Zhang; Bo Song; Haohan Wang; Bo Han 003; Tongliang Liu; Lei Liu 003; Masashi Sugiyama (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Does Confusion Really Hurt Novel Class Discovery? - Haoang Chi; Wenjing Yang 002; Feng Liu 003; Long Lan; Tao Qin; Bo Han 003 (Int. J. Comput. Vis., 2024)

Tackling Noisy Labels With Network Parameter Additive Decomposition. - Jingyi Wang; Xiaobo Xia; Long Lan; Xinghao Wu; Jun Yu 001; Wenjing Yang 002; Bo Han 003; Tongliang Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Efficient private SCO for heavy-tailed data via averaged clipping. - Chenhan Jin; Kaiwen Zhou; Bo Han 003; James Cheng; Tieyong Zeng (Mach. Learn., 2024)

Searching to Exploit Memorization Effect in Deep Learning With Noisy Labels. - Hansi Yang; Quanming Yao; Bo Han 003; James T. Kwok (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection. - Zhipeng Zou; Sheng Wan; Guangyu Li; Bo Han 003; Tongliang Liu; Lin Zhao 003; Chen Gong 002 (AAAI, 2025)

Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models. - Xinyu Pang; Ruixin Hong; Zhanke Zhou; Fangrui Lv; Xinwei Yang; Zhilong Liang; Bo Han 003; Changshui Zhang (COLING, 2025)

Characterizing Submanifold Region for Out-of-Distribution Detection: (Extended Abstract). - Xuhui Li; Zhen Fang 001; Yonggang Zhang 003; Ning Ma; Jiajun Bu; Bo Han 003; Haishuai Wang (ICDE, 2025)

Noisy Test-Time Adaptation in Vision-Language Models. - Chentao Cao; Zhun Zhong; Zhanke Zhou; Tongliang Liu; Yang Liu 018; Kun Zhang 001; Bo Han 003 (ICLR, 2025)

Towards Out-of-Modal Generalization without Instance-level Modal Correspondence. - Zhuo Huang; Gang Niu 001; Bo Han 003; Masashi Sugiyama; Tongliang Liu (ICLR, 2025)

Understanding and Enhancing the Transferability of Jailbreaking Attacks. - Runqi Lin; Bo Han 003; Fengwang Li; Tongliang Liu (ICLR, 2025)

Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection. - Lei Shen; Zhenheng Tang; Lijun Wu; Yonggang Zhang 003; Xiaowen Chu 001; Tao Qin; Bo Han 003 (ICLR, 2025)

Towards Effective Evaluations and Comparisons for LLM Unlearning Methods. - Qizhou Wang; Bo Han 003; Puning Yang; Jianing Zhu; Tongliang Liu; Masashi Sugiyama (ICLR, 2025)

Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond. - Qizhou Wang; Jin Peng Zhou; Zhanke Zhou; Saebyeol Shin; Bo Han 003; Kilian Q. Weinberger (ICLR, 2025)

A Robust Method to Discover Causal or Anticausal Relation. - Yu Yao 005; Yang Zhou; Bo Han 003; Mingming Gong; Kun Zhang 001; Tongliang Liu (ICLR, 2025)

Instance-dependent Early Stopping. - Suqin Yuan; Runqi Lin; Lei Feng 006; Bo Han 003; Tongliang Liu (ICLR, 2025)

Fast and Accurate Blind Flexible Docking. - Zizhuo Zhang; Lijun Wu; Kaiyuan Gao; Jiangchao Yao; Tao Qin; Bo Han 003 (ICLR, 2025)

Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Generation. - Yikun Zhang; Geyan Ye; Chaohao Yuan; Bo Han 003; Long-Kai Huang; Jianhua Yao 001; Wei Liu 005; Yu Rong 001 (ICLR, 2025)

Trustworthy AI under Imperfect Web Data. - Jiangchao Yao; Feng Liu 003; Bo Han 003 (WWW (Companion Volume), 2025)

Trustworthy Machine Learning: From Data to Models. - Bo Han 003; Jiangchao Yao; Tongliang Liu; Bo Li; Sanmi Koyejo; Feng Liu (Found. Trends Priv. Secur., 2025)

InsGNN: Interpretable spatio-temporal graph neural networks via information bottleneck. - Hui Fang; Haishuai Wang; Yang Gao 024; Yonggang Zhang 003; Jiajun Bu; Bo Han 003; Hui Lin (Inf. Fusion, 2025)

A Robust Co-Training Framework to Handle Noisy Labels for Remote Sensing Image Segmentation. - Mitsuyoshi Otsu; Jun Zhou 001; Bo Han 003; Arnon Accad; Shirui Pan (IEEE Trans. Geosci. Remote. Sens., 2025)

Federated Generalized Novel Category Discovery with Prompts Tuning. - Lei Shen; Nan Pu; Zhun Zhong; Mingming Gong; Dianhai Yu; Chengqi Zhang; Bo Han 003 (Trans. Mach. Learn. Res., 2025)

Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs. - Linhao Huang; Xue Jiang; Zhiqiang Wang; Wentao Mo; Xi Xiao; Bo Han 003; Yongjie Yin; Feng Zheng (CoRR, 2025)

Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization. - Zhuo Huang; Muyang Li; Li Shen 008; Jun Yu 001; Chen Gong 002; Bo Han 003; Tongliang Liu (Int. J. Comput. Vis., 2025)

Estimating Per-Class Statistics for Label Noise Learning. - Wenshui Luo; Shuo Chen 003; Tongliang Liu; Bo Han 003; Gang Niu 001; Masashi Sugiyama; Dacheng Tao; Chen Gong 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Characterizing Submanifold Region for Out-of-Distribution Detection. - Xuhui Li; Zhen Fang 001; Yonggang Zhang 003; Ning Ma; Jiajun Bu; Bo Han 003; Haishuai Wang (IEEE Trans. Knowl. Data Eng., 2025)

Federated Noisy Client Learning. - Kahou Tam; Li Li 064; Bo Han 003; Cheng-Zhong Xu 001; Huazhu Fu (IEEE Trans. Neural Networks Learn. Syst., 2025)

Enhancing Sample Selection by Cutting Mislabeled Easy Examples. - Suqin Yuan; Lei Feng 006; Bo Han 003; Tongliang Liu (CoRR, 2025)

One-shot Federated Learning Methods: A Practical Guide. - Xiang Liu 017; Zhenheng Tang; Xia Li 005; Yijun Song; Sijie Ji; Zemin Liu; Bo Han 003; Linshan Jiang; Jialin Li 001 (CoRR, 2025)

Can Large Language Models Help Experimental Design for Causal Discovery? - Junyi Li; Yongqiang Chen 002; Chenxi Liu; Qianyi Cai; Tongliang Liu; Bo Han 003; Kun Zhang 001; Hui Xiong 001 (CoRR, 2025)

GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models. - Yue Wang; Qizhou Wang; Feng Liu 003; Wei Huang; Yali Du 001; Xiaojiang Du; Bo Han 003 (CoRR, 2025)

Classifying Long-tailed and Label-noise Data via Disentangling and Unlearning. - Chen Shu; Mengke Li 001; Yiqun Zhang 006; Yang Lu 009; Bo Han 003; Yiu-ming Cheung; Hanzi Wang (CoRR, 2025)

Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models. - Zhanke Zhou; Zhaocheng Zhu; Xuan Li 005; Mikhail Galkin 001; Xiao Feng 003; Sanmi Koyejo; Jian Tang 005; Bo Han 003 (CoRR, 2025)

Learning to Instruct for Visual Instruction Tuning. - Zhihan Zhou 002; Feng Hong 004; Jiaan Luo; Jiangchao Yao; Dongsheng Li; Bo Han 003; Ya Zhang 002; Yanfeng Wang 001 (CoRR, 2025)

Alignclip: navigating the misalignments for robust vision-language generalization. - Zhongyi Han; Gongxu Luo; Hao Sun; Yaqian Li; Bo Han 003; Mingming Gong; Kun Zhang 001; Tongliang Liu (Mach. Learn., 2025)

Robust Learning under Hybrid Noise. - Yang Wei; Shuo Chen 003; Shanshan Ye; Bo Han 003; Chen Gong 002 (ACM Trans. Intell. Syst. Technol., 2025)

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning. - Puning Yang; Qizhou Wang; Zhuo Huang; Tongliang Liu; Chengqi Zhang; Bo Han 003 (CoRR, 2025)

On the Thinking-Language Modeling Gap in Large Language Models. - Chenxi Liu; Yongqiang Chen 002; Tongliang Liu; James Cheng; Bo Han 003; Kun Zhang 001 (CoRR, 2025)

Learning without Isolation: Pathway Protection for Continual Learning. - Zhikang Chen; Abudukelimu Wuerkaixi; Sen Cui; Haoxuan Li 001; Ding Li; Jingfeng Zhang; Bo Han 003; Gang Niu 001; Houfang Liu; Yi Yang 039; Sifan Yang; Changshui Zhang; et al. (CoRR, 2025)

Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment? - Hongzheng Yang; Yongqiang Chen 002; Zeyu Qin; Tongliang Liu; Chaowei Xiao; Kun Zhang 001; Bo Han 003 (CoRR, 2025)

W-DOE: Wasserstein Distribution-Agnostic Outlier Exposure. - Qizhou Wang; Bo Han 003; Yang Liu 007; Chen Gong 002; Tongliang Liu; Jiming Liu 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium. - Xie Yi; Zhanke Zhou; Chentao Cao; Qiyu Niu; Tongliang Liu; Bo Han 003 (CoRR, 2025)

From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information? - Zhanke Zhou; Xiao Feng 003; Zhaocheng Zhu; Jiangchao Yao; Sanmi Koyejo; Bo Han 003 (CoRR, 2025)

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need. - Ziming Hong; Runnan Chen; Zengmao Wang; Bo Han 003; Bo Du 001; Tongliang Liu (CoRR, 2025)

Co-Reward: Self-supervised Reinforcement Learning for Large Language Model Reasoning via Contrastive Agreement. - Zizhuo Zhang; Jianing Zhu; Xinmu Ge; Zihua Zhao; Zhanke Zhou; Xuan Li 005; Xiao Feng 003; Jiangchao Yao; Bo Han 003 (CoRR, 2025)

Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning. - Ali Taheri Ghahrizjani; Alireza Taban; Shanshan Ye; Abdolreza Mirzaei; Tongliang Liu; Bo Han 003 (CoRR, 2025)

Unknown-Aware Bilateral Dependency Optimization for Defending Against Model Inversion Attacks. - Xiong Peng; Feng Liu 003; Nannan Wang 001; Long Lan; Tongliang Liu; Yiu-Ming Cheung; Bo Han 003 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)