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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
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2020 – today
- 2024
[j25]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)- [j24]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) - [j23]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) - [j22]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) - [j21]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) - [j20]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) - [j19]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) - [c103]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 - [i142]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) - [i141]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) - [i140]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) - [i139]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) - [i138]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) - [i137]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) - [i136]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) - [i135]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) - [i134]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) - [i133]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) - [i132]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) - [i131]Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu:
Few-Shot Adversarial Prompt Learning on Vision-Language Models. CoRR abs/2403.14774 (2024) - [i130]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) - [i129]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. CoRR abs/2404.04865 (2024) - [i128]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) - [i127]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) - [i126]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) - [i125]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) - [i124]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) - 2023
- [j18]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) - [j17]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) - [j16]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) - [j15]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) - [j14]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) - [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) - [c102]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 - [c101]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 - [c100]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 - [c99]Yang Lu, Yiliang Zhang, Bo Han, Yiu-Ming Cheung, Hanzi Wang:
Label-Noise Learning with Intrinsically Long-Tailed Data. ICCV 2023: 1369-1378 - [c98]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 - [c97]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 - [c96]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 - [c95]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 - [c94]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 - [c93]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 - [c92]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 - [c91]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 - [c90]Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han:
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. ICLR 2023 - [c89]Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. ICML 2023: 6786-6817 - [c88]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 - [c87]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 - [c86]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 - [c85]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 - [c84]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 - [c83]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 - [c82]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 - [c81]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 - [c80]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 - [c79]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 - [c78]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 - [c77]Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu:
InstanT: Semi-supervised Learning with Instance-dependent Thresholds. NeurIPS 2023 - [c76]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-distribution Detection. NeurIPS 2023 - [c75]Zige Wang, Yonggang Zhang, Zhen Fang, Long Lan, Wenjing Yang, Bo Han:
SODA: Robust Training of Test-Time Data Adaptors. NeurIPS 2023 - [c74]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 - [c73]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 - [c72]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 - [c71]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 - [i123]Jiangchao Yao, Bo Han, Zhihan Zhou, Ya Zhang, Ivor W. Tsang:
Latent Class-Conditional Noise Model. CoRR abs/2302.09595 (2023) - [i122]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) - [i121]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) - [i120]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) - [i119]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) - [i118]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) - [i117]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) - [i116]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) - [i115]Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. CoRR abs/2304.13976 (2023) - [i114]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) - [i113]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) - [i112]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) - [i111]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) - [i110]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) - [i109]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) - [i108]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) - [i107]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) - [i106]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) - [i105]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) - [i104]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) - [i103]Chaojian Yu, Xiaolong Shi, Jun Yu, Bo Han, Tongliang Liu:
On the Onset of Robust Overfitting in Adversarial Training. CoRR abs/2310.00607 (2023) - [i102]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) - [i101]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) - [i100]Runqi Lin, Chaojian Yu, Bo Han, Tongliang Liu:
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. CoRR abs/2310.08847 (2023) - [i99]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) - [i98]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) - [i97]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) - [i96]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) - [i95]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) - [i94]Zhihan Zhou, Jiangchao Yao, Feng Hong, Ya Zhang, Bo Han, Yanfeng Wang:
Combating Representation Learning Disparity with Geometric Harmonization. CoRR abs/2310.17622 (2023) - [i93]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) - [i92]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) - [i91]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) - [i90]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) - [i89]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) - [i88]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) - [i87]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) - [i86]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) - [i85]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) - [c70]Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c69]Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu:
Fair Classification with Instance-dependent Label Noise. CLeaR 2022: 927-943 - [c68]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 - [c67]Guohao Ying, Xin He, Bin Gao, Bo Han, Xiaowen Chu:
EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs. ECCV (16) 2022: 37-53 - [c66]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 - [c65]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 - [c64]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 - [c63]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c62]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c61]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 - [c60]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 - [c59]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 - [c58]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 - [c57]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 - [c56]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 - [c55]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 - [c54]Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu:
Modeling Adversarial Noise for Adversarial Training. ICML 2022: 27353-27366 - [c53]Zhihan Zhou, Jiangchao Yao, Yan-Feng Wang, Bo Han, Ya Zhang:
Contrastive Learning with Boosted Memorization. ICML 2022: 27367-27377 - [c52]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Du Bo, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. IJCAI 2022: 3688-3694 - [c51]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 - [c50]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 - [c49]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 - [c48]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 - [c47]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 - [c46]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 - [c45]Sen Cui, Jingfeng Zhang, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang:
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. NeurIPS 2022 - [c44]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? NeurIPS 2022 - [c43]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 - [c42]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 - [c41]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. NeurIPS 2022 - [c40]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Gaussian Mixture Models. NeurIPS 2022 - [c39]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. NeurIPS 2022 - [i84]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) - [i83]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) - [i82]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) - [i81]Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok:
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. CoRR abs/2205.03059 (2022) - [i80]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) - [i79]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) - [i78]Zhihan Zhou, Jiangchao Yao, Yanfeng Wang, Bo Han, Ya Zhang:
Contrastive Learning with Boosted Memorization. CoRR abs/2205.12693 (2022) - [i77]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. CoRR abs/2205.13972 (2022) - [i76]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) - [i75]Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han:
Learning Adaptive Propagation for Knowledge Graph Reasoning. CoRR abs/2205.15319 (2022) - [i74]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) - [i73]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) - [i72]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) - [i71]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) - [i70]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) - [i69]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) - [i68]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) - [i67]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) - [i66]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) - [i65]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) - [i64]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) - [i63]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) - [i62]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) - [i61]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) - [i60]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? CoRR abs/2210.14707 (2022) - [i59]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) - [i58]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) - [i57]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) - [c38]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 - [c37]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. AAAI 2021: 10192-10200 - [c36]Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han:
HyperGraph Convolution Based Attributed HyperGraph Clustering. CIKM 2021: 453-463 - [c35]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 - [c34]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 - [c33]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. ICLR 2021 - [c32]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML 2021: 825-836 - [c31]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 - [c30]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 - [c29]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 - [c28]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-noise Learning without Anchor Points. ICML 2021: 6403-6413 - [c27]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 - [c26]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 - [c25]Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Jingren Zhou, Hongxia Yang:
Device-Cloud Collaborative Learning for Recommendation. KDD 2021: 3865-3874 - [c24]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 - [c23]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 - [c22]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 - [c21]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 - [c20]Zhuo Huang, Chao Xue, Bo Han, Jian Yang, Chen Gong:
Universal Semi-Supervised Learning. NeurIPS 2021: 26714-26725 - [i56]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) - [i55]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) - [i54]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) - [i53]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) - [i52]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) - [i51]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. CoRR abs/2103.09468 (2021) - [i50]Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Jingren Zhou, Hongxia Yang:
Device-Cloud Collaborative Learning for Recommendation. CoRR abs/2104.06624 (2021) - [i49]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) - [i48]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) - [i47]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) - [i46]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) - [i45]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) - [i44]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) - [i43]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) - [i42]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) - [i41]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) - [i40]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) - [i39]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) - [i38]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) - [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
- [c19]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 - [c18]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 - [c17]Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama:
Learning with Multiple Complementary Labels. ICML 2020: 3072-3081 - [c16]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 - [c15]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
Variational Imitation Learning with Diverse-quality Demonstrations. ICML 2020: 9407-9417 - [c14]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 - [c13]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 - [c12]Yijing Luo, Bo Han, Chen Gong:
A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery. IJCAI 2020: 2605-2611 - [c11]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. NeurIPS 2020 - [c10]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 - [c9]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 - [c8]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) - [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
[j22] [j21] [j20] [i140] [i139] [i138] [i137] [i135] [i134] [i132] [i131] [i130] [i127] [i125] [i124] [j17] [j15] [j13] [c101] [c98] [c97] [c94] [c93] [c92] [c91] [c90] [c88] [c87] [c86] [c85] [c82] [c81] [c79] [c78] [c77] [c74] [c73] [c71] [i122] [i120] [i119] [i118] [i112] [i111] [i110] [i109] [i107] [i106] [i105] [i104] [i103] [i101] [i100] [i98] [i96] [i95] [i93] [i89] [i88] [j10] [j9] [j7] [c70] [c69] [c68] [c66] [c65] [c64] [c63] [c62] [c61] [c60] [c57] [c56] [c55] [c54] [c52] [c51] [c49] [c48] [c47] [c46] [c42] [c41] [c40] [c39] [i84] [i83] [i82] [i80] [i77] [i76] [i74] [i72] [i71] [i68] [i65] [i64] [i62] [i61] [i59] [i58] [c38] [c37] [c34] [c32] [c31] [c29] [c28] [c27] [c26] [c24] [c23] [c22] [c21] [i56] [i55] [i54] [i53] [i52] [i51] [i49] [i48] [i47] [i46] [i45] [i44] [i43] [i42] [i41] [i40] [i39] [i38] [i36] [i35] [i34] [c10] [c9] [i30] [i29] [i28] [i26] [i25] [i24] [i20] [i18] [i16] [c4] [i11] [i8]
[i128] [j18] [j15] [c88] [c86] [c71] [i114] [i109] [i107] [i105] [i98] [j10] [j8] [j7] [c70] [c68] [c65] [c64] [c63] [c62] [c61] [c60] [c59] [c57] [c48] [i72] [i70] [i58] [c38] [c33] [c32] [c31] [c30] [c29] [c28] [c27] [c24] [c22] [c21] [i56] [i55] [i54] [i53] [i52] [i49] [i48] [i47] [i46] [i45] [i42] [i38] [i37] [i36] [i35] [c19] [c18] [c17] [c16] [c14] [c13] [c11] [c10] [c9] [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]
[j21] [i128] [j18] [c88] [c71] [i114] [i112] [i109] [i105] [i98] [j10] [j7] [c70] [c68] [c65] [c64] [c63] [c62] [c48] [c45] [i72] [i58] [c33] [c32] [c31] [c30] [c29] [c28] [c22] [i55] [i54] [i53] [i48] [i47] [i46] [i38] [c17] [c16] [c15] [c13] [c11] [c10] [c9] [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]
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