default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDBo Han 0003
Person information
- affiliation: Hong Kong Baptist University, Department of Computer Science, Hong Kong
- affiliation: University of Technology Sydney, Centre for Artificial Intelligence, NSW, Australia
Other persons with the same name
- Bo Han — disambiguation page
- Bo Han 0001
![[0000-0001-7042-3322]](https://dblp.org/img/orcid-mark.12x12.png "0000-0001-7042-3322")
— George Mason University, VA, USA (and 2 more) - Bo Han 0002 — Hugo.ai, Surry Hills, Australia (and 1 more)
- Bo Han 0004 — Liaocheng University, School of Mathematical Sciences, China
- Bo Han 0005 — Xi'an JiaoTong University, Xi'an, China
- Bo Han 0006 — Ocean University of China, Qingdao, Shandong, China
- Bo Han 0007 — Harbin Institute of Technology, Department of Mathematics, China
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2025
[j46]Bo Han, Jiangchao Yao, Tongliang Liu, Bo Li, Sanmi Koyejo, Feng Liu:
Trustworthy Machine Learning: From Data to Models. Found. Trends Priv. Secur. 7(2-3): 74-246 (2025)- [j45]Zhuo Huang, Muyang Li, Li Shen, Jun Yu, Chen Gong, Bo Han, Tongliang Liu:
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization. Int. J. Comput. Vis. 133(1): 456-474 (2025) - [j44]Hui Fang, Haishuai Wang, Yang Gao, Yonggang Zhang, Jiajun Bu, Bo Han, Hui Lin:
InsGNN: Interpretable spatio-temporal graph neural networks via information bottleneck. Inf. Fusion 119: 102997 (2025) - [j43]Zhongyi Han, Gongxu Luo, Hao Sun, Yaqian Li, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu:
Alignclip: navigating the misalignments for robust vision-language generalization. Mach. Learn. 114(3): 58 (2025) - [j42]Wenshui Luo, Shuo Chen, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama, Dacheng Tao, Chen Gong:
Estimating Per-Class Statistics for Label Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 47(1): 305-322 (2025) - [j41]Qizhou Wang, Bo Han, Yang Liu, Chen Gong, Tongliang Liu, Jiming Liu:
W-DOE: Wasserstein Distribution-Agnostic Outlier Exposure. IEEE Trans. Pattern Anal. Mach. Intell. 47(5): 3530-3545 (2025) - [j40]Mitsuyoshi Otsu, Jun Zhou, Bo Han, Arnon Accad, Shirui Pan:
A Robust Co-Training Framework to Handle Noisy Labels for Remote Sensing Image Segmentation. IEEE Trans. Geosci. Remote. Sens. 63: 1-14 (2025) - [j39]Yang Wei, Shuo Chen, Shanshan Ye, Bo Han, Chen Gong:
Robust Learning under Hybrid Noise. ACM Trans. Intell. Syst. Technol. 16(2): 34:1-34:27 (2025) - [j38]Xuhui Li, Zhen Fang, Yonggang Zhang, Ning Ma, Jiajun Bu, Bo Han, Haishuai Wang:
Characterizing Submanifold Region for Out-of-Distribution Detection. IEEE Trans. Knowl. Data Eng. 37(1): 130-147 (2025) - [j37]Kahou Tam, Li Li, Bo Han, Cheng-Zhong Xu, Huazhu Fu:
Federated Noisy Client Learning. IEEE Trans. Neural Networks Learn. Syst. 36(1): 1799-1812 (2025) - [c154]Zhipeng Zou, Sheng Wan, Guangyu Li, Bo Han, Tongliang Liu, Lin Zhao, Chen Gong:
Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection. AAAI 2025: 13483-13491 - [c153]Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, Changshui Zhang:
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models. COLING 2025: 11274-11289 - [c152]Chentao Cao, Zhun Zhong, Zhanke Zhou, Tongliang Liu, Yang Liu, Kun Zhang, Bo Han:
Noisy Test-Time Adaptation in Vision-Language Models. ICLR 2025 - [c151]Zhuo Huang, Gang Niu, Bo Han, Masashi Sugiyama, Tongliang Liu:
Towards Out-of-Modal Generalization without Instance-level Modal Correspondence. ICLR 2025 - [c150]Lei Shen, Zhenheng Tang, Lijun Wu, Yonggang Zhang, Xiaowen Chu, Tao Qin, Bo Han:
Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection. ICLR 2025 - [c149]Yu Yao, Yang Zhou, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu:
A Robust Method to Discover Causal or Anticausal Relation. ICLR 2025 - [c148]Suqin Yuan, Runqi Lin, Lei Feng, Bo Han, Tongliang Liu:
Instance-dependent Early Stopping. ICLR 2025 - [c147]Zizhuo Zhang, Lijun Wu, Kaiyuan Gao, Jiangchao Yao, Tao Qin, Bo Han:
Fast and Accurate Blind Flexible Docking. ICLR 2025 - [c146]Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han, Long-Kai Huang, Jianhua Yao, Wei Liu, Yu Rong:
Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Generation. ICLR 2025 - [c145]Jiangchao Yao, Feng Liu, Bo Han:
Trustworthy AI under Imperfect Web Data. WWW (Companion Volume) 2025: 65-68 - [i180]Linhao Huang, Xue Jiang, Zhiqiang Wang, Wentao Mo, Xi Xiao, Bo Han, Yongjie Yin, Feng Zheng:
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs. CoRR abs/2501.01042 (2025) - [i179]Suqin Yuan, Runqi Lin, Lei Feng, Bo Han, Tongliang Liu:
Instance-dependent Early Stopping. CoRR abs/2502.07547 (2025) - [i178]Suqin Yuan, Lei Feng, Bo Han, Tongliang Liu:
Enhancing Sample Selection by Cutting Mislabeled Easy Examples. CoRR abs/2502.08227 (2025) - [i177]Xiang Liu, Zhenheng Tang, Xia Li, Yijun Song, Sijie Ji, Zemin Liu, Bo Han, Linshan Jiang, Jialin Li:
One-shot Federated Learning Methods: A Practical Guide. CoRR abs/2502.09104 (2025) - [i176]Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama:
Accurate Forgetting for Heterogeneous Federated Continual Learning. CoRR abs/2502.14205 (2025) - [i175]Chentao Cao, Zhun Zhong, Zhanke Zhou, Tongliang Liu, Yang Liu, Kun Zhang, Bo Han:
Noisy Test-Time Adaptation in Vision-Language Models. CoRR abs/2502.14604 (2025) - [i174]Zizhuo Zhang, Lijun Wu, Kaiyuan Gao, Jiangchao Yao, Tao Qin, Bo Han:
Fast and Accurate Blind Flexible Docking. CoRR abs/2502.14934 (2025) - [i173]Qizhou Wang, Jin Peng Zhou, Zhanke Zhou, Saebyeol Shin, Bo Han, Kilian Q. Weinberger:
Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond. CoRR abs/2502.19301 (2025) - [i172]Junyi Li, Yongqiang Chen, Chenxi Liu, Qianyi Cai, Tongliang Liu, Bo Han, Kun Zhang, Hui Xiong:
Can Large Language Models Help Experimental Design for Causal Discovery? CoRR abs/2503.01139 (2025) - [i171]Yue Wang, Qizhou Wang, Feng Liu, Wei Huang, Yali Du, Xiaojiang Du, Bo Han:
GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models. CoRR abs/2503.09117 (2025) - [i170]Chen Shu, Mengke Li, Yiqun Zhang, Yang Lu, Bo Han, Yiu-ming Cheung, Hanzi Wang:
Classifying Long-tailed and Label-noise Data via Disentangling and Unlearning. CoRR abs/2503.11414 (2025) - [i169]Zhanke Zhou, Zhaocheng Zhu, Xuan Li, Mikhail Galkin, Xiao Feng, Sanmi Koyejo, Jian Tang, Bo Han:
Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models. CoRR abs/2503.22165 (2025) - [i168]Zhihan Zhou, Feng Hong, Jiaan Luo, Jiangchao Yao, Dongsheng Li, Bo Han, Ya Zhang, Yanfeng Wang:
Learning to Instruct for Visual Instruction Tuning. CoRR abs/2503.22215 (2025) - 2024
- [j36]Haoang Chi, Wenjing Yang, Feng Liu, Long Lan, Tao Qin, Bo Han:
Does Confusion Really Hurt Novel Class Discovery? Int. J. Comput. Vis. 132(8): 3191-3207 (2024) - [j35]Xingrui Yu, Bo Han, Ivor W. Tsang:
USN: A Robust Imitation Learning Method against Diverse Action Noise. J. Artif. Intell. Res. 79: 1237-1280 (2024) - [j34]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. J. Mach. Learn. Res. 25: 84:1-84:83 (2024) - [j33]Senlin Shu, Haobo Wang, Zhuowei Wang, Bo Han, Tao Xiang, Bo An, Lei Feng:
Online binary classification from similar and dissimilar data. Mach. Learn. 113(6): 3463-3484 (2024) - [j32]Fei Zhang, Yunjie Ye, Lei Feng, Zhongwen Rao, Jieming Zhu, Marcus Kalander, Chen Gong, Jianye Hao, Bo Han:
Exploiting counter-examples for active learning with partial labels. Mach. Learn. 113(6): 3849-3868 (2024) - [j31]Chenhan Jin, Kaiwen Zhou, Bo Han, James Cheng, Tieyong Zeng:
Efficient private SCO for heavy-tailed data via averaged clipping. Mach. Learn. 113(11): 8487-8532 (2024) - [j30]Ting Zhou, Hanshu Yan, Bo Han, Lei Liu, Jingfeng Zhang:
Learning a robust foundation model against clean-label data poisoning attacks at downstream tasks. Neural Networks 169: 756-763 (2024) - [j29]Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu:
Regularly Truncated M-Estimators for Learning With Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3522-3536 (2024) - [j28]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(6): 4398-4409 (2024) - [j27]Songhua Wu, Tianyi Zhou, Yuxuan Du, Jun Yu, Bo Han, Tongliang Liu:
A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(7): 4830-4842 (2024) - [j26]Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu:
Tackling Noisy Labels With Network Parameter Additive Decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 46(9): 6341-6354 (2024) - [j25]Hansi Yang, Quanming Yao, Bo Han, James T. Kwok:
Searching to Exploit Memorization Effect in Deep Learning With Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 7833-7849 (2024) - [j24]Weiming Mai, Jiangchao Yao, Chen Gong, Ya Zhang, Yiu-Ming Cheung, Bo Han:
Server-Client Collaborative Distillation for Federated Reinforcement Learning. ACM Trans. Knowl. Discov. Data 18(1): 9:1-9:22 (2024) - [j23]Wankou Yang, Jiren Mai, Fei Zhang, Tongliang Liu, Bo Han:
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. Trans. Mach. Learn. Res. 2024 (2024) - [j22]Wei Yao, Zhanke Zhou, Zhicong Li, Bo Han, Yong Liu:
Understanding Fairness Surrogate Functions in Algorithmic Fairness. Trans. Mach. Learn. Res. 2024 (2024) - [j21]Yongfeng Zhong, Haoang Chi, Feng Liu, Xiao-Ming Wu, Bo Han:
HiFE: Hierarchical Feature Ensemble Framework for Few-shot Hypotheses Adaptation. Trans. Mach. Learn. Res. 2024 (2024) - [j20]Mingyu Li, Tao Zhou, Bo Han, Tongliang Liu, Xinkai Liang, Jiajia Zhao, Chen Gong:
Class-Wise Contrastive Prototype Learning for Semi-Supervised Classification Under Intersectional Class Mismatch. IEEE Trans. Multim. 26: 8145-8156 (2024) - [c144]Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang:
Federated Learning with Extremely Noisy Clients via Negative Distillation. AAAI 2024: 14184-14192 - [c143]Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng:
Enhancing Evolving Domain Generalization through Dynamic Latent Representations. AAAI 2024: 16040-16048 - [c142]Zihua Zhao, Mengxi Chen, Tianjie Dai, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang:
Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning. CVPR 2024: 27371-27380 - [c141]Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. ICLR 2024 - [c140]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. ICLR 2024 - [c139]Xuan Li, Zhanke Zhou, Jiangchao Yao, Yu Rong, Lu Zhang, Bo Han:
Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel. ICLR 2024 - [c138]Runqi Lin, Chaojian Yu, Bo Han, Tongliang Liu:
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. ICLR 2024 - [c137]Jun Nie, Yonggang Zhang, Zhen Fang, Tongliang Liu, Bo Han, Xinmei Tian:
Out-of-Distribution Detection with Negative Prompts. ICLR 2024 - [c136]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu:
FedImpro: Measuring and Improving Client Update in Federated Learning. ICLR 2024 - [c135]Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama:
Accurate Forgetting for Heterogeneous Federated Continual Learning. ICLR 2024 - [c134]Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng:
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. ICLR 2024 - [c133]Shuhai Zhang, Yiliao Song, Jiahao Yang, Yuanqing Li, Bo Han, Mingkui Tan:
Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy. ICLR 2024 - [c132]Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han:
Robust Training of Federated Models with Extremely Label Deficiency. ICLR 2024 - [c131]Jiyang Zheng, Yu Yao, Bo Han, Dadong Wang, Tongliang Liu:
Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation. ICLR 2024 - [c130]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. ICLR 2024 - [c129]Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han:
Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs. ICLR 2024 - [c128]Yongqiang Chen, Yatao Bian, Bo Han, James Cheng:
How Interpretable Are Interpretable Graph Neural Networks? ICML 2024 - [c127]Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han:
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection. ICML 2024 - [c126]Muyang Li, Xiaobo Xia, Runze Wu, Fengming Huang, Jun Yu, Bo Han, Tongliang Liu:
Towards Realistic Model Selection for Semi-supervised Learning. ICML 2024 - [c125]Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu:
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency. ICML 2024 - [c124]Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han:
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. ICML 2024 - [c123]Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu:
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. ICML 2024 - [c122]Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu:
Mitigating Label Noise on Graphs via Topological Sample Selection. ICML 2024 - [c121]Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang:
Balancing Similarity and Complementarity for Federated Learning. ICML 2024 - [c120]Zeyu Ling, Bo Han, Yongkang Wong, Han Lin, Mohan S. Kankanhalli, Weidong Geng:
MCM: Multi-condition Motion Synthesis Framework. IJCAI 2024: 1083-1091 - [c119]Bo Han:
Trustworthy Machine Learning under Imperfect Data. IJCAI 2024: 8535-8540 - [c118]Haoang Chi, He Li, Wenjing Yang, Feng Liu, Long Lan, Xiaoguang Ren, Tongliang Liu, Bo Han:
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? NeurIPS 2024 - [c117]Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang:
Discovery of the Hidden World with Large Language Models. NeurIPS 2024 - [c116]Jiaan Luo, Feng Hong, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang:
Revive Re-weighting in Imbalanced Learning by Density Ratio Estimation. NeurIPS 2024 - [c115]Xiong Peng, Bo Han, Feng Liu, Tongliang Liu, Mingyuan Zhou:
Pseudo-Private Data Guided Model Inversion Attacks. NeurIPS 2024 - [c114]Zhenheng Tang, Yonggang Zhang, Peijie Dong, Yiu-ming Cheung, Amelie Chi Zhou, Bo Han, Xiaowen Chu:
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. NeurIPS 2024 - [c113]Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han:
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning. NeurIPS 2024 - [c112]Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang:
A Sober Look at the Robustness of CLIPs to Spurious Features. NeurIPS 2024 - [c111]Geng Yu, Jianing Zhu, Jiangchao Yao, Bo Han:
Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection. NeurIPS 2024 - [c110]Boxuan Zhang, Jianing Zhu, Zengmao Wang, Tongliang Liu, Bo Du, Bo Han:
What If the Input is Expanded in OOD Detection? NeurIPS 2024 - [c109]Zhanke Zhou, Rong Tao, Jianing Zhu, Yiwen Luo, Zengmao Wang, Bo Han:
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales? NeurIPS 2024 - [c108]Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu:
Few-Shot Adversarial Prompt Learning on Vision-Language Models. NeurIPS 2024 - [i167]Binghui Xie, Yongqiang Chen, Jiaqi Wang, Kaiwen Zhou, Bo Han, Wei Meng, James Cheng:
Enhancing Evolving Domain Generalization through Dynamic Latent Representations. CoRR abs/2401.08464 (2024) - [i166]Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng:
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. CoRR abs/2402.03139 (2024) - [i165]Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang:
Discovery of the Hidden World with Large Language Models. CoRR abs/2402.03941 (2024) - [i164]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu:
FedImpro: Measuring and Improving Client Update in Federated Learning. CoRR abs/2402.07011 (2024) - [i163]Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han:
Robust Training of Federated Models with Extremely Label Deficiency. CoRR abs/2402.14430 (2024) - [i162]Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. CoRR abs/2402.15070 (2024) - [i161]Shuhai Zhang, Yiliao Song, Jiahao Yang, Yuanqing Li, Bo Han, Mingkui Tan:
Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy. CoRR abs/2402.16041 (2024) - [i160]Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu:
Mitigating Label Noise on Graph via Topological Sample Selection. CoRR abs/2403.01942 (2024) - [i159]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. CoRR abs/2403.08840 (2024) - [i158]Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han:
Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs. CoRR abs/2403.10231 (2024) - [i157]Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang:
Do CLIPs Always Generalize Better than ImageNet Models? CoRR abs/2403.11497 (2024) - [i156]Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu:
Tackling Noisy Labels with Network Parameter Additive Decomposition. CoRR abs/2403.13241 (2024) - [i155]Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu:
Few-Shot Adversarial Prompt Learning on Vision-Language Models. CoRR abs/2403.14774 (2024) - [i154]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. CoRR abs/2403.20078 (2024) - [i153]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. CoRR abs/2404.04865 (2024) - [i152]Zeyu Ling, Bo Han, Yongkang Wang, Han Lin, Mohan S. Kankanhalli, Weidong Geng:
MCM: Multi-condition Motion Synthesis Framework. CoRR abs/2404.12886 (2024) - [i151]Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han, Long-Kai Huang, Jianhua Yao, Wei Liu, Yu Rong:
Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation. CoRR abs/2404.16880 (2024) - [i150]Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang:
Balancing Similarity and Complementarity for Federated Learning. CoRR abs/2405.09892 (2024) - [i149]Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu:
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency. CoRR abs/2405.16262 (2024) - [i148]Zihua Zhao, Mengxi Chen, Tianjie Dai, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang:
Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning. CoRR abs/2405.16996 (2024) - [i147]Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han:
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. CoRR abs/2405.18786 (2024) - [i146]Ziqing Fan, Ruipeng Zhang, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang:
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data. CoRR abs/2405.18972 (2024) - [i145]Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu:
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. CoRR abs/2405.19919 (2024) - [i144]Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han:
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection. CoRR abs/2406.00806 (2024) - [i143]Yongqiang Chen, Yatao Bian, Bo Han, James Cheng:
How Interpretable Are Interpretable Graph Neural Networks? CoRR abs/2406.07955 (2024) - [i142]Jianing Zhu, Bo Han, Jiangchao Yao, Jianliang Xu, Gang Niu, Masashi Sugiyama:
Decoupling the Class Label and the Target Concept in Machine Unlearning. CoRR abs/2406.08288 (2024) - [i141]Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama:
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. CoRR abs/2406.09179 (2024) - [i140]Yang Wei, Shuo Chen, Shanshan Ye, Bo Han, Chen Gong:
Robust Learning under Hybrid Noise. CoRR abs/2407.04029 (2024) - [i139]Yu Zheng, Wenchao Zhang, Yonggang Zhang, Wei Song, Kai Zhou, Bo Han:
Rethinking Improved Privacy-Utility Trade-off with Pre-existing Knowledge for DP Training. CoRR abs/2409.03344 (2024) - [i138]Hangyu Li, Yihan Xu, Jiangchao Yao, Nannan Wang, Xinbo Gao, Bo Han:
Knowledge-Enhanced Facial Expression Recognition with Emotional-to-Neutral Transformation. CoRR abs/2409.08598 (2024) - [i137]Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han:
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning. CoRR abs/2410.12474 (2024) - [i136]Boxuan Zhang, Jianing Zhu, Zengmao Wang, Tongliang Liu, Bo Du, Bo Han:
What If the Input is Expanded in OOD Detection? CoRR abs/2410.18472 (2024) - [i135]Zhenheng Tang, Yonggang Zhang, Peijie Dong, Yiu-ming Cheung, Amelie Chi Zhou, Bo Han, Xiaowen Chu:
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. CoRR abs/2410.20380 (2024) - [i134]Zhanke Zhou, Rong Tao, Jianing Zhu, Yiwen Luo, Zengmao Wang, Bo Han:
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales? CoRR abs/2410.23856 (2024) - [i133]Geng Yu, Jianing Zhu, Jiangchao Yao, Bo Han:
Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection. CoRR abs/2411.03359 (2024) - [i132]Zhanke Zhou, Jianing Zhu, Fengfei Yu, Xuan Li, Xiong Peng, Tongliang Liu, Bo Han:
Model Inversion Attacks: A Survey of Approaches and Countermeasures. CoRR abs/2411.10023 (2024) - [i131]Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung, Bo Han, Xinmei Tian:
Detecting Discrepancies Between AI-Generated and Natural Images Using Uncertainty. CoRR abs/2412.05897 (2024) - [i130]Shuhai Zhang, Jiahao Yang, Hui Luo, Jie Chen, Li Wang, Feng Liu, Bo Han, Mingkui Tan:
Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds. CoRR abs/2412.08394 (2024) - [i129]Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, Changshui Zhang:
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models. CoRR abs/2412.13791 (2024) - 2023
- [j19]Chen Gong, Yongliang Ding, Bo Han, Gang Niu, Jian Yang, Jane You, Dacheng Tao, Masashi Sugiyama:
Class-Wise Denoising for Robust Learning Under Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 2835-2848 (2023) - [j18]Xiaobo Xia, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao, Tongliang Liu:
Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 3047-3058 (2023) - [j17]Jiangchao Yao, Bo Han, Zhihan Zhou, Ya Zhang, Ivor W. Tsang:
Latent Class-Conditional Noise Model. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 9964-9980 (2023) - [j16]Shuo Yang, Songhua Wu, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
A Parametrical Model for Instance-Dependent Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 14055-14068 (2023) - [j15]Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han:
GRACE: A General Graph Convolution Framework for Attributed Graph Clustering. ACM Trans. Knowl. Discov. Data 17(3): 31:1-31:31 (2023) - [j14]Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang:
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions. Trans. Mach. Learn. Res. 2023 (2023) - [j13]Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han:
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. Trans. Mach. Learn. Res. 2023 (2023) - [j12]Hongxin Wei, Renchunzi Xie, Lei Feng, Bo Han, Bo An:
Deep Learning From Multiple Noisy Annotators as A Union. IEEE Trans. Neural Networks Learn. Syst. 34(12): 10552-10562 (2023) - [c107]Xin He, Jiangchao Yao, Yuxin Wang, Zhenheng Tang, Ka Chun Cheung, Simon See, Bo Han, Xiaowen Chu:
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension. AAAI 2023: 7839-7847 - [c106]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CVPR 2023: 16175-16185 - [c105]Huantong Li, Xiangmiao Wu, Fanbing Lv, Daihai Liao, Thomas H. Li, Yonggang Zhang, Bo Han, Mingkui Tan:
Hard Sample Matters a Lot in Zero-Shot Quantization. CVPR 2023: 24417-24426 - [c104]Yang Lu, Yiliang Zhang, Bo Han, Yiu-Ming Cheung, Hanzi Wang:
Label-Noise Learning with Intrinsically Long-Tailed Data. ICCV 2023: 1369-1378 - [c103]Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu:
Holistic Label Correction for Noisy Multi-Label Classification. ICCV 2023: 1483-1493 - [c102]Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu:
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples. ICCV 2023: 1833-1843 - [c101]Ke Liu, Feng Liu, Haishuai Wang, Ning Ma, Jiajun Bu, Bo Han:
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis. ICCV 2023: 5451-5460 - [c100]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng:
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. ICLR 2023 - [c99]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. ICLR 2023 - [c98]Yong Lin, Renjie Pi, Weizhong Zhang, Xiaobo Xia, Jiahui Gao, Xiao Zhou, Tongliang Liu, Bo Han:
A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond. ICLR 2023 - [c97]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. ICLR 2023 - [c96]Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu:
Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning. ICLR 2023 - [c95]Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han:
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. ICLR 2023 - [c94]Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. ICML 2023: 6786-6817 - [c93]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML 2023: 8260-8275 - [c92]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Detecting Out-of-distribution Data through In-distribution Class Prior. ICML 2023: 15067-15088 - [c91]Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen:
A Universal Unbiased Method for Classification from Aggregate Observations. ICML 2023: 36804-36820 - [c90]Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu:
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? ICML 2023: 39660-39673 - [c89]Shuhai Zhang, Feng Liu, Jiahao Yang, Yifan Yang, Changsheng Li, Bo Han, Mingkui Tan:
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score. ICML 2023: 41429-41451 - [c88]Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao, Quanming Yao, Bo Han:
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation. ICML 2023: 42843-42877 - [c87]Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han:
Exploring Model Dynamics for Accumulative Poisoning Discovery. ICML 2023: 42983-43004 - [c86]Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han:
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability. ICML 2023: 43068-43104 - [c85]Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han:
AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning. KDD 2023: 3446-3457 - [c84]Zhihan Zhou, Jiangchao Yao, Feng Hong, Ya Zhang, Bo Han, Yanfeng Wang:
Combating Representation Learning Disparity with Geometric Harmonization. NeurIPS 2023 - [c83]Yingbin Bai, Zhongyi Han, Erkun Yang, Jun Yu, Bo Han, Dadong Wang, Tongliang Liu:
Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping. NeurIPS 2023 - [c82]Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng:
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? NeurIPS 2023 - [c81]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Understanding and Improving Feature Learning for Out-of-Distribution Generalization. NeurIPS 2023 - [c80]Ziqing Fan, Ruipeng Zhang, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang:
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data. NeurIPS 2023 - [c79]Zhuo Huang, Li Shen, Jun Yu, Bo Han, Tongliang Liu:
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning. NeurIPS 2023 - [c78]Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu:
InstanT: Semi-supervised Learning with Instance-dependent Thresholds. NeurIPS 2023 - [c77]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-distribution Detection. NeurIPS 2023 - [c76]Zige Wang, Yonggang Zhang, Zhen Fang, Long Lan, Wenjing Yang, Bo Han:
SODA: Robust Training of Test-Time Data Adaptors. NeurIPS 2023 - [c75]Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han:
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. NeurIPS 2023 - [c74]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. NeurIPS 2023 - [c73]Zhanke Zhou, Jiangchao Yao, Jiaxu Liu, Xiawei Guo, Quanming Yao, LI He, Liang Wang, Bo Zheng, Bo Han:
Combating Bilateral Edge Noise for Robust Link Prediction. NeurIPS 2023 - [c72]Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. NeurIPS 2023 - [i128]Jiangchao Yao, Bo Han, Zhihan Zhou, Ya Zhang, Ivor W. Tsang:
Latent Class-Conditional Noise Model. CoRR abs/2302.09595 (2023) - [i127]Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han:
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. CoRR abs/2303.00250 (2023) - [i126]Xinyi Shang, Gang Huang, Yang Lu, Jian Lou, Bo Han, Yiu-ming Cheung, Hanzi Wang:
Federated Semi-Supervised Learning with Annotation Heterogeneity. CoRR abs/2303.02445 (2023) - [i125]Jiren Mai, Fei Zhang, Junjie Ye, Marcus Kalander, Xian Zhang, Wankou Yang, Tongliang Liu, Bo Han:
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. CoRR abs/2303.02449 (2023) - [i124]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. CoRR abs/2303.05033 (2023) - [i123]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CoRR abs/2303.13087 (2023) - [i122]Huantong Li, Xiangmiao Wu, Fanbing Lv, Daihai Liao, Thomas H. Li, Yonggang Zhang, Bo Han, Mingkui Tan:
Hard Sample Matters a Lot in Zero-Shot Quantization. CoRR abs/2303.13826 (2023) - [i121]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Towards Understanding Feature Learning in Out-of-Distribution Generalization. CoRR abs/2304.11327 (2023) - [i120]Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. CoRR abs/2304.13976 (2023) - [i119]Jingfeng Zhang, Bo Song, Bo Han, Lei Liu, Gang Niu, Masashi Sugiyama:
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. CoRR abs/2305.00399 (2023) - [i118]Shuhai Zhang, Feng Liu, Jiahao Yang, Yifan Yang, Changsheng Li, Bo Han, Mingkui Tan:
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score. CoRR abs/2305.16035 (2023) - [i117]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. CoRR abs/2305.18377 (2023) - [i116]Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han:
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability. CoRR abs/2306.03715 (2023) - [i115]Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han:
Exploring Model Dynamics for Accumulative Poisoning Discovery. CoRR abs/2306.03726 (2023) - [i114]Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu:
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR abs/2306.07036 (2023) - [i113]Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao, Quanming Yao, Bo Han:
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation. CoRR abs/2306.09104 (2023) - [i112]Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen:
A Universal Unbiased Method for Classification from Aggregate Observations. CoRR abs/2306.11343 (2023) - [i111]Hui Kang, Sheng Liu, Huaxi Huang, Jun Yu, Bo Han, Dadong Wang, Tongliang Liu:
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels. CoRR abs/2307.05025 (2023) - [i110]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR abs/2307.05948 (2023) - [i109]Fei Zhang, Yunjie Ye, Lei Feng, Zhongwen Rao, Jieming Zhu, Marcus Kalander, Chen Gong, Jianye Hao, Bo Han:
Exploiting Counter-Examples for Active Learning with Partial labels. CoRR abs/2307.07413 (2023) - [i108]Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu:
Regularly Truncated M-estimators for Learning with Noisy Labels. CoRR abs/2309.00894 (2023) - [i107]Zeyu Ling, Bo Han, Yongkang Wong, Mohan S. Kankanhalli, Weidong Geng:
MCM: Multi-condition Motion Synthesis Framework for Multi-scenario. CoRR abs/2309.03031 (2023) - [i106]Chaojian Yu, Xiaolong Shi, Jun Yu, Bo Han, Tongliang Liu:
On the Onset of Robust Overfitting in Adversarial Training. CoRR abs/2310.00607 (2023) - [i105]Zihao Wang, Yongqiang Chen, Yang Duan, Weijiang Li, Bo Han, James Cheng, Hanghang Tong:
Towards out-of-distribution generalizable predictions of chemical kinetics properties. CoRR abs/2310.03152 (2023) - [i104]Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han:
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. CoRR abs/2310.05077 (2023) - [i103]Runqi Lin, Chaojian Yu, Bo Han, Tongliang Liu:
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. CoRR abs/2310.08847 (2023) - [i102]Zige Wang, Yonggang Zhang, Zhen Fang, Long Lan, Wenjing Yang, Bo Han:
SODA: Robust Training of Test-Time Data Adaptors. CoRR abs/2310.11093 (2023) - [i101]Wei Yao, Zhanke Zhou, Zhicong Li, Bo Han, Yong Liu:
Understanding Fairness Surrogate Functions in Algorithmic Fairness. CoRR abs/2310.11211 (2023) - [i100]Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. CoRR abs/2310.13923 (2023) - [i99]Ke Liu, Feng Liu, Haishuai Wang, Ning Ma, Jiajun Bu, Bo Han:
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis. CoRR abs/2310.14184 (2023) - [i98]Zhuo Huang, Muyang Li, Li Shen, Jun Yu, Chen Gong, Bo Han, Tongliang Liu:
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization. CoRR abs/2310.16391 (2023) - [i97]Zhuo Huang, Li Shen, Jun Yu, Bo Han, Tongliang Liu:
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning. CoRR abs/2310.16412 (2023) - [i96]Zhihan Zhou, Jiangchao Yao, Feng Hong, Ya Zhang, Bo Han, Yanfeng Wang:
Combating Representation Learning Disparity with Geometric Harmonization. CoRR abs/2310.17622 (2023) - [i95]Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu:
InstanT: Semi-supervised Learning with Instance-dependent Thresholds. CoRR abs/2310.18910 (2023) - [i94]Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng:
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? CoRR abs/2310.19035 (2023) - [i93]Zhanke Zhou, Jiangchao Yao, Jiaxu Liu, Xiawei Guo, Quanming Yao, Li He, Liang Wang, Bo Zheng, Bo Han:
Combating Bilateral Edge Noise for Robust Link Prediction. CoRR abs/2311.01196 (2023) - [i92]Xuan Li, Zhanke Zhou, Jiangchao Yao, Yu Rong, Lu Zhang, Bo Han:
Long-Range Neural Atom Learning for Molecular Graphs. CoRR abs/2311.01276 (2023) - [i91]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-Distribution Detection. CoRR abs/2311.01796 (2023) - [i90]Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han:
DeepInception: Hypnotize Large Language Model to Be Jailbreaker. CoRR abs/2311.03191 (2023) - [i89]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. CoRR abs/2311.03236 (2023) - [i88]Yongqiang Chen, Binghui Xie, Kaiwen Zhou, Bo Han, Yatao Bian, James Cheng:
Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes. CoRR abs/2311.18194 (2023) - [i87]Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang:
Federated Learning with Extremely Noisy Clients via Negative Distillation. CoRR abs/2312.12703 (2023) - [i86]Songming Zhang, Yuxiao Luo, Qizhou Wang, Haoang Chi, Weikai Li, Bo Han, Jinyan Li:
Are All Unseen Data Out-of-Distribution? CoRR abs/2312.16243 (2023) - 2022
- [j11]Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok:
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. J. Mach. Learn. Res. 23: 136:1-136:60 (2022) - [j10]Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama:
Learning from Noisy Pairwise Similarity and Unlabeled Data. J. Mach. Learn. Res. 23: 307:1-307:34 (2022) - [j9]Chen Gong, Qizhou Wang, Tongliang Liu, Bo Han, Jane You, Jian Yang, Dacheng Tao:
Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(8): 4163-4177 (2022) - [j8]Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long:
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. Trans. Mach. Learn. Res. 2022 (2022) - [j7]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama:
NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. 2022 (2022) - [c71]Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c70]Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu:
Fair Classification with Instance-dependent Label Noise. CLeaR 2022: 927-943 - [c69]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR 2022: 16609-16618 - [c68]Guohao Ying, Xin He, Bin Gao, Bo Han, Xiaowen Chu:
EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs. ECCV (16) 2022: 37-53 - [c67]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR 2022 - [c66]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c65]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR 2022 - [c64]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c63]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c62]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c61]Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang:
Reliable Adversarial Distillation with Unreliable Teachers. ICLR 2022 - [c60]Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng:
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. ICML 2022: 7144-7163 - [c59]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu:
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. ICML 2022: 21111-21132 - [c58]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. ICML 2022: 25302-25312 - [c57]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. ICML 2022: 25595-25610 - [c56]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu:
Improving Adversarial Robustness via Mutual Information Estimation. ICML 2022: 27338-27352 - [c55]Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu:
Modeling Adversarial Noise for Adversarial Training. ICML 2022: 27353-27366 - [c54]Zhihan Zhou, Jiangchao Yao, Yanfeng Wang, Bo Han, Ya Zhang:
Contrastive Learning with Boosted Memorization. ICML 2022: 27367-27377 - [c53]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Du Bo, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. IJCAI 2022: 3688-3694 - [c52]Xiong Peng, Feng Liu, Jingfeng Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. KDD 2022: 1358-1367 - [c51]Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen, Bo Han, Jingren Zhou, Hongxia Yang:
Device-cloud Collaborative Recommendation via Meta Controller. KDD 2022: 4353-4362 - [c50]Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS 2022 - [c49]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. NeurIPS 2022 - [c48]Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu:
RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning. NeurIPS 2022 - [c47]De Cheng, Yixiong Ning, Nannan Wang, Xinbo Gao, Heng Yang, Yuxuan Du, Bo Han, Tongliang Liu:
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization. NeurIPS 2022 - [c46]Sen Cui, Jingfeng Zhang, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang:
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. NeurIPS 2022 - [c45]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? NeurIPS 2022 - [c44]Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Kaili Ma, Bo Han, Bo Li, James Cheng:
Exact Shape Correspondence via 2D graph convolution. NeurIPS 2022 - [c43]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attack Against Deep Neural Networks. NeurIPS 2022 - [c42]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. NeurIPS 2022 - [c41]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Gaussian Mixture Models. NeurIPS 2022 - [c40]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. NeurIPS 2022 - [i85]Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu:
Do We Need to Penalize Variance of Losses for Learning with Label Noise? CoRR abs/2201.12739 (2022) - [i84]Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR abs/2202.05441 (2022) - [i83]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR abs/2202.08057 (2022) - [i82]Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok:
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. CoRR abs/2205.03059 (2022) - [i81]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Probabilistic Mixture-of-Experts. CoRR abs/2205.09086 (2022) - [i80]Zhuowei Wang, Tianyi Zhou, Guodong Long, Bo Han, Jing Jiang:
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels. CoRR abs/2205.10110 (2022) - [i79]Zhihan Zhou, Jiangchao Yao, Yanfeng Wang, Bo Han, Ya Zhang:
Contrastive Learning with Boosted Memorization. CoRR abs/2205.12693 (2022) - [i78]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. CoRR abs/2205.13972 (2022) - [i77]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. CoRR abs/2205.14826 (2022) - [i76]Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han:
Learning Adaptive Propagation for Knowledge Graph Reasoning. CoRR abs/2205.15319 (2022) - [i75]Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu:
MSR: Making Self-supervised learning Robust to Aggressive Augmentations. CoRR abs/2206.01999 (2022) - [i74]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu:
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. CoRR abs/2206.02465 (2022) - [i73]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR abs/2206.02791 (2022) - [i72]Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. CoRR abs/2206.05483 (2022) - [i71]Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng:
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. CoRR abs/2206.07314 (2022) - [i70]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng:
Pareto Invariant Risk Minimization. CoRR abs/2206.07766 (2022) - [i69]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. CoRR abs/2206.08675 (2022) - [i68]Chenhan Jin, Kaiwen Zhou, Bo Han, James Cheng, Ming-Chang Yang:
Efficient Private SCO for Heavy-Tailed Data via Clipping. CoRR abs/2206.13011 (2022) - [i67]Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen, Bo Han, Jingren Zhou, Hongxia Yang:
Device-Cloud Collaborative Recommendation via Meta Controller. CoRR abs/2207.03066 (2022) - [i66]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. CoRR abs/2207.03162 (2022) - [i65]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu:
Improving Adversarial Robustness via Mutual Information Estimation. CoRR abs/2207.12203 (2022) - [i64]Yiliang Zhang, Yang Lu, Bo Han, Yiu-ming Cheung, Hanzi Wang:
Combating Noisy-Labeled and Imbalanced Data by Two Stage Bi-Dimensional Sample Selection. CoRR abs/2208.09833 (2022) - [i63]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attacks against Deep Neural Networks. CoRR abs/2209.14826 (2022) - [i62]Chaojian Yu, Dawei Zhou, Li Shen, Jun Yu, Bo Han, Mingming Gong, Nannan Wang, Tongliang Liu:
Strength-Adaptive Adversarial Training. CoRR abs/2210.01288 (2022) - [i61]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? CoRR abs/2210.14707 (2022) - [i60]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. CoRR abs/2210.15198 (2022) - [i59]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR abs/2211.00269 (2022) - [i58]Xin He, Jiangchao Yao, Yuxin Wang, Zhenheng Tang, Ka Chun Cheung, Simon See, Bo Han, Xiaowen Chu:
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension. CoRR abs/2211.12759 (2022) - 2021
- [j6]Bo Han, Ivor W. Tsang, Xiaokui Xiao, Ling Chen, Sai-Fu Fung, Celina Ping Yu:
Privacy-Preserving Stochastic Gradual Learning. IEEE Trans. Knowl. Data Eng. 33(8): 3129-3140 (2021) - [c39]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. AAAI 2021: 10183-10191 - [c38]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. AAAI 2021: 10192-10200 - [c37]Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han:
HyperGraph Convolution Based Attributed HyperGraph Clustering. CIKM 2021: 453-463 - [c36]Chuang Zhang, Qizhou Wang, Tengfei Liu, Xun Lu, Jin Hong, Bo Han, Chen Gong:
Fraud Detection under Multi-Sourced Extremely Noisy Annotations. CIKM 2021: 2497-2506 - [c35]Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang:
Robust early-learning: Hindering the memorization of noisy labels. ICLR 2021 - [c34]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. ICLR 2021 - [c33]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML 2021: 825-836 - [c32]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. ICML 2021: 2880-2891 - [c31]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. ICML 2021: 3252-3262 - [c30]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML 2021: 3564-3575 - [c29]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-noise Learning without Anchor Points. ICML 2021: 6403-6413 - [c28]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. ICML 2021: 11285-11295 - [c27]Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao:
Towards Defending against Adversarial Examples via Attack-Invariant Features. ICML 2021: 12835-12845 - [c26]Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Jingren Zhou, Hongxia Yang:
Device-Cloud Collaborative Learning for Recommendation. KDD 2021: 3865-3874 - [c25]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. NeurIPS 2021: 4409-4420 - [c24]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. NeurIPS 2021: 20970-20982 - [c23]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [c22]Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu:
Understanding and Improving Early Stopping for Learning with Noisy Labels. NeurIPS 2021: 24392-24403 - [c21]Zhuo Huang, Chao Xue, Bo Han, Jian Yang, Chen Gong:
Universal Semi-Supervised Learning. NeurIPS 2021: 26714-26725 - [i57]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. CoRR abs/2101.05467 (2021) - [i56]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. CoRR abs/2102.01886 (2021) - [i55]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-Noise Learning without Anchor Points. CoRR abs/2102.02400 (2021) - [i54]Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama:
Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR abs/2102.03482 (2021) - [i53]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. CoRR abs/2102.04002 (2021) - [i52]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. CoRR abs/2103.09468 (2021) - [i51]Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Jingren Zhou, Hongxia Yang:
Device-Cloud Collaborative Learning for Recommendation. CoRR abs/2104.06624 (2021) - [i50]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Label-noise Transition Matrix using DNNs. CoRR abs/2105.13001 (2021) - [i49]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama:
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR abs/2105.14676 (2021) - [i48]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR abs/2106.00445 (2021) - [i47]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Instance Correction for Learning with Open-set Noisy Labels. CoRR abs/2106.00455 (2021) - [i46]Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang:
Reliable Adversarial Distillation with Unreliable Teachers. CoRR abs/2106.04928 (2021) - [i45]Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao:
Towards Defending against Adversarial Examples via Attack-Invariant Features. CoRR abs/2106.05036 (2021) - [i44]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Jun Yu, Xiaoyu Wang, Tongliang Liu:
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training. CoRR abs/2106.05453 (2021) - [i43]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness through the Lens of Causality. CoRR abs/2106.06196 (2021) - [i42]Chenhong Zhou, Feng Liu, Chen Gong, Tongliang Liu, Bo Han, William Kwok-Wai Cheung:
KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation. CoRR abs/2106.06237 (2021) - [i41]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. CoRR abs/2106.06326 (2021) - [i40]Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang:
PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels. CoRR abs/2106.07451 (2021) - [i39]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR abs/2106.07904 (2021) - [i38]Li Li, Huazhu Fu, Bo Han, Cheng-Zhong Xu, Ling Shao:
Federated Noisy Client Learning. CoRR abs/2106.13239 (2021) - [i37]Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng:
Local Reweighting for Adversarial Training. CoRR abs/2106.15776 (2021) - [i36]Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu:
Understanding and Improving Early Stopping for Learning with Noisy Labels. CoRR abs/2106.15853 (2021) - [i35]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. CoRR abs/2109.02986 (2021) - [i34]Dawei Zhou, Nannan Wang, Tongliang Liu, Bo Han:
Modelling Adversarial Noise for Adversarial Defense. CoRR abs/2109.09901 (2021) - [i33]Zeyuan Chen, Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Wei Zhang, Hongxia Yang:
MC$^2$-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation. CoRR abs/2109.12314 (2021) - [i32]Yujie Pan, Jiangchao Yao, Bo Han, Kunyang Jia, Ya Zhang, Hongxia Yang:
Click-through Rate Prediction with Auto-Quantized Contrastive Learning. CoRR abs/2109.13921 (2021) - [i31]Guohao Ying, Xin He, Bin Gao, Bo Han, Xiaowen Chu:
EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs. CoRR abs/2111.15097 (2021) - 2020
- [c20]Chao Li, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, Qibin Zhao:
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling. AAAI 2020: 4602-4609 - [c19]Chun Wang, Bo Han, Shirui Pan, Jing Jiang, Gang Niu, Guodong Long:
Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure. ICDM 2020: 571-580 - [c18]Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama:
Learning with Multiple Complementary Labels. ICML 2020: 3072-3081 - [c17]Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. ICML 2020: 4006-4016 - [c16]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
Variational Imitation Learning with Diverse-quality Demonstrations. ICML 2020: 9407-9417 - [c15]Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok:
Searching to Exploit Memorization Effect in Learning with Noisy Labels. ICML 2020: 10789-10798 - [c14]Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. ICML 2020: 11278-11287 - [c13]Yijing Luo, Bo Han, Chen Gong:
A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery. IJCAI 2020: 2605-2611 - [c12]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. NeurIPS 2020 - [c11]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Part-dependent Label Noise: Towards Instance-dependent Label Noise. NeurIPS 2020 - [c10]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. NeurIPS 2020 - [c9]Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, Guodong Long:
Confusable Learning for Large-Class Few-Shot Classification. ECML/PKDD (2) 2020: 707-723 - [i30]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. CoRR abs/2001.03772 (2020) - [i29]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Towards Mixture Proportion Estimation without Irreducibility. CoRR abs/2002.03673 (2020) - [i28]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Multi-Class Classification from Noisy-Similarity-Labeled Data. CoRR abs/2002.06508 (2020) - [i27]Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. CoRR abs/2002.11242 (2020) - [i26]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. CoRR abs/2006.07805 (2020) - [i25]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A New Perspective on Learning with Label Noise. CoRR abs/2006.07831 (2020) - [i24]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Parts-dependent Label Noise: Towards Instance-dependent Label Noise. CoRR abs/2006.07836 (2020) - [i23]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. CoRR abs/2007.08929 (2020) - [i22]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. CoRR abs/2010.01736 (2020) - [i21]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. CoRR abs/2010.01875 (2020) - [i20]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy is Aware of Adversarial Attacks. CoRR abs/2010.11415 (2020) - [i19]Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, Guodong Long:
Confusable Learning for Large-class Few-Shot Classification. CoRR abs/2011.03154 (2020) - [i18]Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama:
A Survey of Label-noise Representation Learning: Past, Present and Future. CoRR abs/2011.04406 (2020) - [i17]Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long:
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. CoRR abs/2012.00925 (2020) - [i16]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao:
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels. CoRR abs/2012.00932 (2020)
2010 – 2019
- 2019
- [j5]Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama:
Millionaire: a hint-guided approach for crowdsourcing. Mach. Learn. 108(5): 831-858 (2019) - [j4]Bo Han, Ivor W. Tsang, Ling Chen, Joey Tianyi Zhou, Celina Ping Yu:
Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels. IEEE Trans. Neural Networks Learn. Syst. 30(12): 3774-3787 (2019) - [c8]Dawei Du, Yue Zhang, Liefeng Bo, Hailin Shi, Rui Zhu, Bo Han, Chunhui Zhang, Guizhong Liu, Han Wu, Hao Wen, Haoran Wang, Pengfei Zhu, Jiaqing Fan, Jie Chen, Jie Gao, Jie Zhang, Jinghao Zhou, Jinliu Zhou, Jinwang Wang, Jiuqing Wan, Josef Kittler, Kaihua Zhang, Longyin Wen, Kaiqi Huang, Kang Yang, Kangkai Zhang, Lianghua Huang, Lijun Zhou, Lingling Shi, Lu Ding, Ning Wang, Peng Wang, Qintao Hu, Xiao Bian, Robert Laganière, Ruiyan Ma, Ruohan Zhang, Shanrong Zou, Shengwei Zhao, Shengyang Li, Shengyin Zhu, Shikun Li, Shiming Ge, Shiyu Xuan, Haibin Ling, Tianyang Xu, Ting He, Wei Shi, Wei Song, Weiming Hu, Wenhua Zhang, Wenjun Zhu, Xi Yu, Xianhai Wang, Xiaojun Wu, Qinghua Hu, Xiaotong Li, Xiaoxue Li, Xiaoyue Yin, Xin Zhang, Xin Zhao, Xizhe Xue, Xu Lei, Xueyuan Yang, Yanjie Gao, Yanyun Zhao, Jiayu Zheng, Yinda Xu, Ying Li, Yong Wang, Yong Yang, Yuting Yang, Yuxuan Li, Zeyu Wang, Zhenhua Feng, Zhipeng Zhang, Zhiyong Yu, Tao Peng, Zhizhao Duan, Zhuojin Sun, Xinyao Wang:
VisDrone-SOT2019: The Vision Meets Drone Single Object Tracking Challenge Results. ICCV Workshops 2019: 199-212 - [c7]Quanming Yao, James Tin-Yau Kwok, Bo Han:
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. ICML 2019: 7035-7044 - [c6]Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
How does Disagreement Help Generalization against Label Corruption? ICML 2019: 7164-7173 - [c5]Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan S. Kankanhalli:
Towards Robust ResNet: A Small Step but a Giant Leap. IJCAI 2019: 4285-4291 - [c4]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? NeurIPS 2019: 6835-6846 - [i15]Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
How does Disagreement Help Generalization against Label Corruption? CoRR abs/1901.04215 (2019) - [i14]Miao Xu, Bingcong Li, Gang Niu, Bo Han, Masashi Sugiyama:
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. CoRR abs/1901.10155 (2019) - [i13]Jingfeng Zhang, Bo Han, Laura Wynter, Kian Hsiang Low, Mohan S. Kankanhalli:
Towards Robust ResNet: A Small Step but A Giant Leap. CoRR abs/1902.10887 (2019) - [i12]Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama:
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. CoRR abs/1905.07720 (2019) - [i11]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? CoRR abs/1906.00189 (2019) - [i10]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
VILD: Variational Imitation Learning with Diverse-quality Demonstrations. CoRR abs/1909.06769 (2019) - [i9]Hansi Yang, Quanming Yao, Bo Han, Gang Niu:
Searching to Exploit Memorization Effect in Learning from Corrupted Labels. CoRR abs/1911.02377 (2019) - [i8]Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? CoRR abs/1911.08696 (2019) - 2018
- [j3]Bo Han, Yuangang Pan, Ivor W. Tsang:
Robust Plackett-Luce model for k-ary crowdsourced preferences. Mach. Learn. 107(4): 675-702 (2018) - [j2]Yuangang Pan, Bo Han, Ivor W. Tsang:
Stagewise learning for noisy k-ary preferences. Mach. Learn. 107(8-10): 1333-1361 (2018) - [j1]Bo Han, Ivor W. Tsang, Ling Chen, Celina Ping Yu, Sai-Fu Fung:
Progressive Stochastic Learning for Noisy Labels. IEEE Trans. Neural Networks Learn. Syst. 29(10): 5136-5148 (2018) - [c3]Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama:
Masking: A New Perspective of Noisy Supervision. NeurIPS 2018: 5841-5851 - [c2]Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-teaching: Robust training of deep neural networks with extremely noisy labels. NeurIPS 2018: 8536-8546 - [i7]Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama:
Millionaire: A Hint-guided Approach for Crowdsourcing. CoRR abs/1802.09172 (2018) - [i6]Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-sampling: Training Robust Networks for Extremely Noisy Supervision. CoRR abs/1804.06872 (2018) - [i5]Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama:
Masking: A New Perspective of Noisy Supervision. CoRR abs/1805.08193 (2018) - [i4]Miao Xu, Gang Niu, Bo Han, Ivor W. Tsang, Zhi-Hua Zhou, Masashi Sugiyama:
Matrix Co-completion for Multi-label Classification with Missing Features and Labels. CoRR abs/1805.09156 (2018) - [i3]Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. CoRR abs/1809.11008 (2018) - [i2]Bo Han, Ivor W. Tsang, Xiaokui Xiao, Ling Chen, Sai-Fu Fung, Celina Ping Yu:
Privacy-preserving Stochastic Gradual Learning. CoRR abs/1810.00383 (2018) - 2016
- [c1]Bo Han, Ivor W. Tsang, Ling Chen:
On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent. ECML/PKDD (1) 2016: 665-680 - [i1]Bo Han, Ivor W. Tsang, Ling Chen:
On the Convergence of A Family of Robust Losses for Stochastic Gradient Descent. CoRR abs/1605.01623 (2016)
Coauthor Index
Yiu-Ming Cheung
aka: Yiu-ming Cheung
aka: Yiu-ming Cheung
[c145] [i171] [j36] [j34] [j21] [c140] [c124] [c118] [c115] [c113] [i154] [i153] [i147] [i137] [i130] [j13] [c101] [c97] [c93] [c92] [c89] [c77] [c74] [i124] [i118] [i110] [i99] [i91] [i89] [c66] [c60] [c52] [c45] [c42] [i72] [i71] [i61] [i60] [c30] [c24] [c23] [i53] [i42] [i41] [i39] [i37] [i20] [i12]
[j46] [j45] [j43] [j42] [j41] [c154] [c152] [c151] [c149] [c148] [i179] [i178] [i175] [i172] [j29] [j28] [j27] [j26] [j23] [j20] [c141] [c140] [c138] [c137] [c136] [c132] [c131] [c130] [c127] [c126] [c125] [c124] [c123] [c122] [c118] [c117] [c115] [c113] [c110] [c108] [i165] [i164] [i163] [i162] [i160] [i159] [i156] [i155] [i154] [i149] [i147] [i145] [i144] [i141] [i137] [i136] [i132] [i131] [j18] [j16] [j14] [j13] [c106] [c103] [c102] [c99] [c98] [c97] [c96] [c95] [c93] [c92] [c91] [c90] [c87] [c86] [c83] [c79] [c78] [c75] [c74] [c72] [i127] [i125] [i124] [i123] [i117] [i116] [i115] [i114] [i112] [i111] [i110] [i108] [i106] [i104] [i103] [i100] [i98] [i97] [i95] [i90] [i89] [j10] [j9] [j7] [c71] [c70] [c69] [c67] [c66] [c65] [c64] [c63] [c62] [c61] [c58] [c57] [c56] [c55] [c53] [c52] [c50] [c49] [c48] [c47] [c43] [c42] [c41] [c40] [i85] [i84] [i83] [i81] [i78] [i77] [i75] [i73] [i72] [i69] [i66] [i65] [i63] [i62] [i60] [i59] [c39] [c38] [c35] [c33] [c32] [c30] [c29] [c28] [c27] [c25] [c24] [c23] [c22] [i57] [i56] [i55] [i54] [i53] [i52] [i50] [i49] [i48] [i47] [i46] [i45] [i44] [i43] [i42] [i41] [i40] [i39] [i36] [i35] [i34] [c11] [c10] [i30] [i29] [i28] [i26] [i25] [i24] [i20] [i18] [i16] [c4] [i11] [i8]
[j42] [c151] [i176] [c135] [c121] [i150] [i142] [j19] [j16] [c93] [c91] [c72] [i119] [i114] [i112] [i110] [i100] [j10] [j8] [j7] [c71] [c69] [c66] [c65] [c64] [c63] [c62] [c61] [c60] [c58] [c49] [i73] [i71] [i59] [c39] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [c25] [c23] [c22] [i57] [i56] [i55] [i54] [i53] [i50] [i49] [i48] [i47] [i46] [i43] [i39] [i37] [i36] [i35] [c20] [c19] [c18] [c17] [c15] [c14] [c12] [c11] [c10] [i30] [i29] [i28] [i27] [i26] [i25] [i24] [i23] [i22] [i21] [i20] [i18] [i17] [c6] [c4] [i15] [i14] [i12] [i11] [i9] [i8] [c3] [c2] [i6] [i5] [i4] [i3]
[j42] [c151] [i176] [j28] [c135] [c121] [i150] [i142] [i141] [j19] [c93] [c72] [i119] [i117] [i114] [i110] [i100] [j10] [j7] [c71] [c69] [c66] [c65] [c64] [c63] [c49] [c46] [i73] [i59] [c34] [c33] [c32] [c31] [c30] [c29] [c23] [i56] [i55] [i54] [i49] [i48] [i47] [i39] [c18] [c17] [c16] [c14] [c12] [c11] [c10] [i30] [i29] [i27] [i26] [i24] [i23] [i22] [i21] [i20] [i18] [j5] [c6] [c4] [i15] [i14] [i12] [i11] [i10] [i8] [c3] [c2] [i7] [i6] [i5] [i4] [i3]
[j46] [c147] [c145] [i174] [i168] [j24] [c142] [c139] [c129] [c123] [c122] [c116] [c111] [i160] [i158] [i148] [i146] [i145] [i142] [i138] [i133] [j17] [c107] [c95] [c88] [c87] [c86] [c84] [c80] [c73] [c72] [i128] [i127] [i116] [i115] [i113] [i100] [i96] [i93] [i92] [i90] [c61] [c54] [c51] [i79] [i67] [i58] [c38] [c26] [i52] [i51] [i46] [i33] [i32] [c6] [i15] [c3] [i5] [i3]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-06-16 21:10 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
