Liu, Tongliang

214 publications

ICML 2025 A Lens into Interpretable Transformer Mistakes via Semantic Dependency Ruo-Jing Dong, Yu Yao, Bo Han, Tongliang Liu

ICLR 2025 A Robust Method to Discover Causal or Anticausal Relation Yu Yao, Yang Zhou, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu

ICML 2025 A Sample Efficient Conditional Independence Test in the Presence of Discretization Boyang Sun, Yu Yao, Xinshuai Dong, Zongfang Liu, Tongliang Liu, Yumou Qiu, Kun Zhang

NeurIPS 2025 AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents Hanjun Luo, Shenyu Dai, Chiming Ni, Xinfeng Li, Guibin Zhang, Kun Wang, Tongliang Liu, Hanan Salam

MLJ 2025 Alignclip: Navigating the Misalignments for Robust Vision-Language Generalization Zhongyi Han, Gongxu Luo, Hao Sun, Yaqian Li, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu

NeurIPS 2025 Aligning What Matters: Masked Latent Adaptation for Text-to-Audio-Video Generation Jiyang Zheng, Siqi Pan, Yu Yao, Zhaoqing Wang, Dadong Wang, Tongliang Liu

NeurIPS 2025 Can Dependencies Induced by LLM-Agent Workflows Be Trusted? Yu Yao, Yiliao Song, Yian Xie, Mengdan Fan, Mingyu Guo, Tongliang Liu

ICLR 2025 Chain-of-Focus Prompting: Leveraging Sequential Visual Cues to Prompt Large Autoregressive Vision Models Jiyang Zheng, Jialiang Shen, Yu Yao, Min Wang, Yang Yang, Dadong Wang, Tongliang Liu

NeurIPS 2025 Cognitive Mirrors: Exploring the Diverse Functional Roles of Attention Heads in LLM Reasoning Xueqi Ma, Jun Wang, Yanbei Jiang, Sarah Monazam Erfani, Tongliang Liu, James Bailey

ICLR 2025 DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception Run Luo, Yunshui Li, Longze Chen, Wanwei He, Ting-En Lin, Ziqiang Liu, Lei Zhang, Zikai Song, Hamid Alinejad-Rokny, Xiaobo Xia, Tongliang Liu, Binyuan Hui, Min Yang

ICLR 2025 Efficient and Trustworthy Causal Discovery with Latent Variables and Complex Relations Xiu-Chuan Li, Tongliang Liu

NeurIPS 2025 Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples Suqin Yuan, Lei Feng, Bo Han, Tongliang Liu

NeurIPS 2025 Epistemic Uncertainty for Generated Image Detection Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung, Bo Han, Xinmei Tian

ICML 2025 Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Puning Yang, Qizhou Wang, Zhuo Huang, Tongliang Liu, Chengqi Zhang, Bo Han

ICLR 2025 Flow: Modularized Agentic Workflow Automation Boye Niu, Yiliao Song, Kai Lian, Yifan Shen, Yu Yao, Kun Zhang, Tongliang Liu

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

NeurIPS 2025 Generative Model Inversion Through the Lens of the Manifold Hypothesis Xiong Peng, Bo Han, Fengfei Yu, Tongliang Liu, Feng Liu, Mingyuan Zhou

ICLR 2025 Instance-Dependent Early Stopping Suqin Yuan, Runqi Lin, Lei Feng, Bo Han, Tongliang Liu

CVPR 2025 Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising Yongli Xiang, Ziming Hong, Lina Yao, Dadong Wang, Tongliang Liu

CVPR 2025 LaVin-DiT: Large Vision Diffusion Transformer Zhaoqing Wang, Xiaobo Xia, Runnan Chen, Dongdong Yu, Changhu Wang, Mingming Gong, Tongliang Liu

IJCAI 2025 Label Distribution Learning with Biased Annotations Assisted by Multi-Label Learning Zhiqiang Kou, Si Qin, Hailin Wang, Jing Wang, Ming-Kun Xie, Shuo Chen, Yuheng Jia, Tongliang Liu, Masashi Sugiyama, Xin Geng

ICLR 2025 Learning Graph Invariance by Harnessing Spuriosity Tianjun Yao, Yongqiang Chen, Kai Hu, Tongliang Liu, Kun Zhang, Zhiqiang Shen

NeurIPS 2025 MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation Jiaxin Huang, Runnan Chen, Ziwen Li, Zhengqing Gao, Xiao He, Yandong Guo, Mingming Gong, Tongliang Liu

ICLR 2025 Noisy Test-Time Adaptation in Vision-Language Models Chentao Cao, Zhun Zhong, Zhanke Zhou, Tongliang Liu, Yang Liu, Kun Zhang, Bo Han

ICLRW 2025 On the Language of Thoughts in Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, James Cheng, Bo Han, Kun Zhang

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

NeurIPS 2025 Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Tianjun Yao, Haoxuan Li, Yongqiang Chen, Tongliang Liu, Le Song, Eric P. Xing, Zhiqiang Shen

NeurIPS 2025 RankMatch: A Novel Approach to Semi-Supervised Label Distribution Learning Leveraging Rank Correlation Between Labels Zhiqiang Kou, Yucheng Xie, Hailin Wang, Junyang Chen, Jing Wang, Ming-Kun Xie, Shuo Chen, Yuheng Jia, Tongliang Liu, Xin Geng

ICML 2025 Ranked from Within: Ranking Large Multimodal Models Without Labels Weijie Tu, Weijian Deng, Dylan Campbell, Yu Yao, Jiyang Zheng, Tom Gedeon, Tongliang Liu

ICLR 2025 Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates Xiu-Chuan Li, Jun Wang, Tongliang Liu

NeurIPS 2025 Revealing Multimodal Causality with Large Language Models Jin Li, Shoujin Wang, Qi Zhang, Feng Liu, Tongliang Liu, Longbing Cao, Shui Yu, Fang Chen

TMLR 2025 State Space Models Can Express $n$-Gram Languages Vinoth Nandakumar, Qiang Qu, Peng Mi, Tongliang Liu

NeurIPS 2025 Surprise3D: A Dataset for Spatial Understanding and Reasoning in Complex 3D Scenes Jiaxin Huang, Ziwen Li, Hanlue Zhang, Runnan Chen, Zhengqing Gao, Xiao He, Yandong Guo, Wenping Wang, Tongliang Liu, Mingming Gong

ICML 2025 Surrogate Prompt Learning: Towards Efficient and Diverse Prompt Learning for Vision-Language Models Liangchen Liu, Nannan Wang, Xi Yang, Xinbo Gao, Tongliang Liu

IJCAI 2025 Toward Robust Non-Transferable Learning: A Survey and Benchmark Ziming Hong, Yongli Xiang, Tongliang Liu

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

ICLR 2025 Towards Out-of-Modal Generalization Without Instance-Level Modal Correspondence Zhuo Huang, Gang Niu, Bo Han, Masashi Sugiyama, Tongliang Liu

ICLR 2025 Understanding and Enhancing the Transferability of Jailbreaking Attacks Runqi Lin, Bo Han, Fengwang Li, Tongliang Liu

ICML 2025 When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap Is All You Need Ziming Hong, Runnan Chen, Zengmao Wang, Bo Han, Bo Du, Tongliang Liu

ICLR 2024 Causal Structure Recovery with Latent Variables Under Milder Distributional and Graphical Assumptions Xiu-Chuan Li, Kun Zhang, Tongliang Liu

NeurIPS 2024 Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization Hongling Zheng, Li Shen, Yong Luo, Tongliang Liu, Jialie Shen, Dacheng Tao

NeurIPSW 2024 DeepInception: Hypnotize Large Language Model to Be Jailbreaker Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han

NeurIPS 2024 Discovery of the Hidden World with Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang

AAAI 2024 E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu

ICLR 2024 Early Stopping Against Label Noise Without Validation Data Suqin Yuan, Lei Feng, Tongliang Liu

CVPR 2024 Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning Wei Zhang, Chaoqun Wan, Tongliang Liu, Xinmei Tian, Xu Shen, Jieping Ye

ICLR 2024 Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation Jiyang Zheng, Yu Yao, Bo Han, Dadong Wang, Tongliang Liu

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

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

TMLR 2024 Exploit CAM by Itself: Complementary Learning System for Weakly Supervised Semantic Segmentation Wankou Yang, Jiren Mai, Fei Zhang, Tongliang Liu, Bo Han

AAAI 2024 Exploring Channel-Aware Typical Features for Out-of-Distribution Detection Rundong He, Yue Yuan, Zhongyi Han, Fan Wang, Wan Su, Yilong Yin, Tongliang Liu, Yongshun Gong

ICLR 2024 FedImpro: Measuring and Improving Client Update in Federated Learning Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu

ICLR 2024 Federated Causal Discovery from Heterogeneous Data Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang

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

ICLRW 2024 How Well Does GPT-4V(ision) Adapt to Distribution Shifts? a Preliminary Investigation Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman Khan, Lina Yao, Tongliang Liu, Kun Zhang

ICLR 2024 IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen, Jiale Liu, Qingyun Wu, Tongliang Liu

JMLR 2024 Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao

ICML 2024 Improving Accuracy-Robustness Trade-Off via Pixel Reweighted Adversarial Training Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu

ICLR 2024 Improving Non-Transferable Representation Learning by Harnessing Content and Style Ziming Hong, Zhenyi Wang, Li Shen, Yu Yao, Zhuo Huang, Shiming Chen, Chuanwu Yang, Mingming Gong, Tongliang Liu

NeurIPS 2024 In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment Dongting Hu, Huan Fu, Jiaxian Guo, Liuhua Peng, Tingjin Chu, Feng Liu, Tongliang Liu, Mingming Gong

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

NeurIPS 2024 Learning the Latent Causal Structure for Modeling Label Noise Yexiong Lin, Yu Yao, Tongliang Liu

ICML 2024 MOKD: Cross-Domain Finetuning for Few-Shot Classification via Maximizing Optimized Kernel Dependence Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-Ming Cheung, Bo Han

ICML 2024 Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu

NeurIPS 2024 Mind the Gap Between Prototypes and Images in Cross-Domain Finetuning Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han

ICML 2024 Mitigating Label Noise on Graphs via Topological Sample Selection Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu

ICLR 2024 Negative Label Guided OOD Detection with Pretrained Vision-Language Models Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

ICLR 2024 Neural Auto-Designer for Enhanced Quantum Kernels Cong Lei, Yuxuan Du, Peng Mi, Jun Yu, Tongliang Liu

ICLR 2024 NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models Beyond Spherical Linear Interpolation PengFei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han

NeurIPS 2024 NoiseGPT: Label Noise Detection and Rectification Through Probability Curvature Haoyu Wang, Zhuo Huang, Zhiwei Lin, Tongliang Liu

TMLR 2024 On Intriguing Layer-Wise Properties of Robust Overfitting in Adversarial Training Duke Nguyen, Chaojian Yu, Vinoth Nandakumar, Young Choon Lee, Tongliang Liu

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

ICML 2024 Optimal Kernel Choice for Score Function-Based Causal Discovery Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong

ICLR 2024 Out-of-Distribution Detection with Negative Prompts Jun Nie, Yonggang Zhang, Zhen Fang, Tongliang Liu, Bo Han, Xinmei Tian

MLJ 2024 ProtoSimi: Label Correction for Fine-Grained Visual Categorization Jialiang Shen, Yu Yao, Shaoli Huang, Zhiyong Wang, Jing Zhang, Ruxing Wang, Jun Yu, Tongliang Liu

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

ICML 2024 Refined Coreset Selection: Towards Minimal Coreset Size Under Model Performance Constraints Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Hongxin Wei, Tongliang Liu

ICLR 2024 Robust Training of Federated Models with Extremely Label Deficiency Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han

ICML 2024 Task-Aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning Yusong Hu, De Cheng, Dingwen Zhang, Nannan Wang, Tongliang Liu, Xinbo Gao

ICML 2024 Towards Realistic Model Selection for Semi-Supervised Learning Muyang Li, Xiaobo Xia, Runze Wu, Fengming Huang, Jun Yu, Bo Han, Tongliang Liu

ECCV 2024 Training a Secure Model Against Data-Free Model Extraction Zhenyi Wang, Li Shen, Junfeng Guo, Tiehang Duan, Siyu Luan, Tongliang Liu, Mingchen Gao

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

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

NeurIPS 2024 What if the Input Is Expanded in OOD Detection? Boxuan Zhang, Jianing Zhu, Zengmao Wang, Tongliang Liu, Bo Du, Bo Han

CVPR 2024 Your Transferability Barrier Is Fragile: Free-Lunch for Transferring the Non-Transferable Learning Ziming Hong, Li Shen, Tongliang Liu

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

ICML 2023 A Universal Unbiased Method for Classification from Aggregate Observations Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen

ICCV 2023 ALIP: Adaptive Language-Image Pre-Training with Synthetic Caption Kaicheng Yang, Jiankang Deng, Xiang An, Jiawei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu

NeurIPS 2023 An Efficient Dataset Condensation Plugin and Its Application to Continual Learning Enneng Yang, Li Shen, Zhenyi Wang, Tongliang Liu, Guibing Guo

CVPR 2023 Architecture, Dataset and Model-Scale Agnostic Data-Free Meta-Learning Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao

CVPR 2023 BiCro: Noisy Correspondence Rectification for Multi-Modality Data via Bi-Directional Cross-Modal Similarity Consistency Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu

NeurIPS 2023 CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation Yexiong Lin, Yu Yao, Xiaolong Shi, Mingming Gong, Xu Shen, Dong Xu, Tongliang Liu

ICLR 2023 Combating Exacerbated Heterogeneity for Robust Models in Federated Learning Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han

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

ICLR 2023 Contextual Convolutional Networks Shuxian Liang, Xu Shen, Tongliang Liu, Xian-Sheng Hua

CVPR 2023 DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao

NeurIPS 2023 Defending Against Data-Free Model Extraction by Distributionally Robust Defensive Training Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, David Doermann, Mingchen Gao

ICML 2023 Detecting Out-of-Distribution Data Through In-Distribution Class Prior Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

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

ICML 2023 Diversity-Enhancing Generative Network for Few-Shot Hypothesis Adaptation Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han

ICML 2023 Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration Dawei Zhou, Yukun Chen, Nannan Wang, Decheng Liu, Xinbo Gao, Tongliang Liu

NeurIPS 2023 Eliminating Catastrophic Overfitting via Abnormal Adversarial Examples Regularization Runqi Lin, Chaojian Yu, Tongliang Liu

ICML 2023 Evolving Semantic Prototype Improves Generative Zero-Shot Learning Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang

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

TMLR 2023 FedDAG: Federated DAG Structure Learning Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard Bondell

NeurIPS 2023 FedFed: Feature Distillation Against Data Heterogeneity in Federated Learning Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han

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

IJCAI 2023 Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, Dacheng Tao

ICLR 2023 Harnessing Out-of-Distribution Examples via Augmenting Content and Style Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu

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

ICCV 2023 HumanMAC: Masked Motion Completion for Human Motion Prediction Ling-Hao Chen, JiaWei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu

NeurIPS 2023 InstanT: Semi-Supervised Learning with Instance-Dependent Thresholds Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu

TMLR 2023 KRADA: Known-Region-Aware Domain Alignment for Open-Set Domain Adaptation in Semantic Segmentation Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, William Cheung, Bo Han

ICCV 2023 Late Stopping: Avoiding Confidently Learning from Mislabeled Examples Suqin Yuan, Lei Feng, Tongliang Liu

ICLR 2023 Moderate Coreset: A Universal Method of Data Selection for Real-World Data-Efficient Deep Learning Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu

ICLR 2023 Mosaic Representation Learning for Self-Supervised Visual Pre-Training Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, Tongliang Liu

ICCV 2023 Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering Dongting Hu, Zhenkai Zhang, Tingbo Hou, Tongliang Liu, Huan Fu, Mingming Gong

TMLR 2023 Noise-Robust Graph Learning by Estimating and Leveraging Pairwise Interactions Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang

NeurIPS 2023 Out-of-Distribution Detection Learning with Unreliable Out-of-Distribution Sources Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han

ICLR 2023 Out-of-Distribution Detection with Implicit Outlier Transformation Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han

ICCV 2023 PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, Tongliang Liu

ICML 2023 Phase-Aware Adversarial Defense for Improving Adversarial Robustness Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu

ICCV 2023 Point-Query Quadtree for Crowd Counting, Localization, and More Chengxin Liu, Hao Lu, Zhiguo Cao, Tongliang Liu

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

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

ICLR 2023 Symmetric Pruning in Quantum Neural Networks Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao

NeurIPS 2023 Towards Label-Free Scene Understanding by Vision Foundation Models Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, Wenping Wang

ICLR 2023 Unicom: Universal and Compact Representation Learning for Image Retrieval Xiang An, Jiankang Deng, Kaicheng Yang, Jaiwei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu

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

ICML 2023 Which Is Better for Learning with Noisy Labels: The Semi-Supervised Method or Modeling Label Noise? Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu

ICLR 2022 Adversarial Robustness Through the Lens of Causality Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang

NeurIPS 2022 Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama

CVPR 2022 CRIS: CLIP-Driven Referring Image Segmentation Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu

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

NeurIPS 2022 Counterfactual Fairness with Partially Known Causal Graph Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong

ICML 2022 Estimating Instance-Dependent Bayes-Label Transition Matrix Using a Deep Neural Network Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu

NeurIPS 2022 Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu

ICLR 2022 Exploiting Class Activation Value for Partial-Label Learning Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama

CVPR 2022 Exploring Set Similarity for Dense Self-Supervised Representation Learning Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu

CLeaR 2022 Fair Classification with Instance-Dependent Label Noise Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu

ICML 2022 Improving Adversarial Robustness via Mutual Information Estimation Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu

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

ICMLW 2022 Invariance Principle Meets Out-of-Distribution Generalization on Graphs Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Ma Kaili, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

CVPR 2022 Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, XuHan Zhu, Jing Yang, Tongliang Liu

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

JMLR 2022 Learning from Noisy Pairwise Similarity and Unlabeled Data Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama

ICLR 2022 Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu

ICLR 2022 Meta Discovery: Learning to Discover Novel Classes Given Very Limited Data Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

NeurIPS 2022 MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell

ICML 2022 Modeling Adversarial Noise for Adversarial Training Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu

CVPR 2022 Mutual Quantization for Cross-Modal Search with Noisy Labels Erkun Yang, Dongren Yao, Tongliang Liu, Cheng Deng

TMLR 2022 NoiLin: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama

NeurIPS 2022 Out-of-Distribution Detection with an Adaptive Likelihood Ratio on Informative Hierarchical VAE Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, Xin Miao, Bo An

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

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

ICLR 2022 Reliable Adversarial Distillation with Unreliable Teachers Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang

ICLR 2022 Rethinking Class-Prior Estimation for Positive-Unlabeled Learning Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao

IJCAI 2022 Robust Weight Perturbation for Adversarial Training Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Du Bo, Tongliang Liu

ICLR 2022 Sample Selection with Uncertainty of Losses for Learning with Noisy Labels Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama

CVPR 2022 Selective-Supervised Contrastive Learning with Noisy Labels Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu

CVPR 2022 SimT: Handling Open-Set Noise for Domain Adaptive Semantic Segmentation Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan

ICML 2022 To Smooth or Not? When Label Smoothing Meets Noisy Labels Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu

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

ICML 2022 Understanding Robust Overfitting of Adversarial Training and Beyond Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu

ICLR 2022 Understanding and Improving Graph Injection Attack by Promoting Unnoticeability Yongqiang Chen, Han Yang, Yonggang Zhang, Ma Kaili, Tongliang Liu, Bo Han, James Cheng

ECCVW 2022 Unleashing the Potential of Adaptation Models via Go-Getting Domain Labels Xin Jin, Tianyu He, Xu Shen, Songhua Wu, Tongliang Liu, Jingwen Ye, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua

NeurIPS 2022 Watermarking for Out-of-Distribution Detection Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han

CVPR 2021 A Second-Order Approach to Learning with Instance-Dependent Label Noise Zhaowei Zhu, Tongliang Liu, Yang Liu

ICCVW 2021 Boosting Fairness for Masked Face Recognition Jun Yu, Xinlong Hao, Zeyu Cui, Peng He, Tongliang Liu

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

ICML 2021 Confidence Scores Make Instance-Dependent Label-Noise Learning Possible Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama

NeurIPS 2021 Confident Anchor-Induced Multi-Source Free Domain Adaptation Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu

NeurIPS 2021 Instance-Dependent Label-Noise Learning Under a Structural Causal Model Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang

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

AAAI 2021 Learning with Group Noise Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han

ICML 2021 Maximum Mean Discrepancy Test Is Aware of Adversarial Attacks Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama

ICCV 2021 Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data Yingbin Bai, Tongliang Liu

NeurIPS 2021 Probabilistic Margins for Instance Reweighting in Adversarial Training Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

ICML 2021 Provably End-to-End Label-Noise Learning Without Anchor Points Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama

ICCV 2021 Removing Adversarial Noise in Class Activation Feature Space Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu

CVPR 2021 Revisiting Knowledge Distillation: An Inheritance and Exploration Framework Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua

ICLR 2021 Robust Early-Learning: Hindering the Memorization of Noisy Labels Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang

NeurIPS 2021 TOHAN: A One-Step Approach Towards Few-Shot Hypothesis Adaptation Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William Cheung, James T. Kwok

AAAI 2021 Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong

ICML 2021 Towards Defending Against Adversarial Examples via Attack-Invariant Features Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao

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

AAAI 2020 Diversified Bayesian Nonnegative Matrix Factorization Maoying Qiao, Jun Yu, Tongliang Liu, Xinchao Wang, Dacheng Tao

NeurIPS 2020 Domain Generalization via Entropy Regularization Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao

NeurIPS 2020 Dual T: Reducing Estimation Error for Transition Matrix in Label-Noise Learning Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama

ICML 2020 Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian

AAAI 2020 Generative-Discriminative Complementary Learning Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich

ICML 2020 LTF: A Label Transformation Framework for Correcting Label Shift Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao

ICML 2020 Label-Noise Robust Domain Adaptation Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao

ICML 2020 Learning with Bounded Instance and Label-Dependent Label Noise Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao

NeurIPS 2020 Part-Dependent Label Noise: Towards Instance-Dependent Label Noise Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama

ECCV 2020 Sub-Center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces Jiankang Deng, Jia Guo, Tongliang Liu, Mingming Gong, Stefanos Zafeiriou

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

NeurIPS 2019 Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence Fengxiang He, Tongliang Liu, Dacheng Tao

IJCAI 2019 Positive and Unlabeled Learning with Label Disambiguation Chuang Zhang, Dexin Ren, Tongliang Liu, Jian Yang, Chen Gong

ECCV 2018 Correcting the Triplet Selection Bias for Triplet Loss Baosheng Yu, Tongliang Liu, Mingming Gong, Changxing Ding, Dacheng Tao

ECCV 2018 Deep Domain Generalization via Conditional Invariant Adversarial Networks Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, Dacheng Tao

AAAI 2018 Domain Generalization via Conditional Invariant Representations Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, Dacheng Tao

ECCV 2018 Learning with Biased Complementary Labels Xiyu Yu, Tongliang Liu, Mingming Gong, Dacheng Tao

IJCAI 2018 Online Heterogeneous Transfer Metric Learning Yong Luo, Tongliang Liu, Yonggang Wen, Dacheng Tao

IJCAI 2018 Quantum Divide-and-Conquer Anchoring for Separable Non-Negative Matrix Factorization Yuxuan Du, Tongliang Liu, Yinan Li, Runyao Duan, Dacheng Tao

AAAI 2018 Reliable Multi-View Clustering Hong Tao, Chenping Hou, Xinwang Liu, Tongliang Liu, Dongyun Yi, Jubo Zhu

IJCAI 2018 Semantic Structure-Based Unsupervised Deep Hashing Erkun Yang, Cheng Deng, Tongliang Liu, Wei Liu, Dacheng Tao

ICML 2017 Algorithmic Stability and Hypothesis Complexity Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao

IJCAI 2017 General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao

CVPR 2017 On Compressing Deep Models by Low Rank and Sparse Decomposition Xiyu Yu, Tongliang Liu, Xinchao Wang, Dacheng Tao

IJCAI 2017 Understanding How Feature Structure Transfers in Transfer Learning Tongliang Liu, Qiang Yang, Dacheng Tao

AAAI 2016 Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair Hao Xiong, Tongliang Liu, Dacheng Tao

ICML 2016 Domain Adaptation with Conditional Transferable Components Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf

IJCAI 2015 Multi-Task Model and Feature Joint Learning Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao