Liu, Weiwei

64 publications

ICLR 2026 Neural Collapse in Multi-Task Learning Youjun Wang, Boqi Li, Xin Zou, Weiwei Liu

TMLR 2026 Quantitative Analysis of the Effect of Density Ratio Estimation in Covariate Shift Adaptation Chenglin Yu, Zhengyu Zhou, Weiwei Liu

ICLR 2026 Two-Layer Convolutional Autoencoders Trained on Normal Data Provably Detect Unseen Anomalies Yanbo Chen, Weiwei Liu

ICML 2025 A Closer Look at Generalized BH Algorithm for Out-of-Distribution Detection Xinsong Ma, Jie Wu, Weiwei Liu

ICML 2025 An Error Analysis of Flow Matching for Deep Generative Modeling Zhengyu Zhou, Weiwei Liu

ICML 2025 An Online Statistical Framework for Out-of-Distribution Detection Xinsong Ma, Xin Zou, Weiwei Liu

ICML 2025 Model Uncertainty Quantification by Conformal Prediction in Continual Learning Rui Gao, Weiwei Liu

ICML 2025 Nonconvex Theory of $m$-Estimators with Decomposable Regularizers Weiwei Liu

NeurIPS 2025 On the SAC-BL Algorithm for Anomaly Detection Xinsong Ma, Jie Wu, Weiwei Liu

ICML 2025 Towards Understanding Catastrophic Forgetting in Two-Layer Convolutional Neural Networks Boqi Li, Youjun Wang, Weiwei Liu

NeurIPS 2024 A Boosting-Type Convergence Result for AdaBoost.MH with Factorized Multi-Class Classifiers Xin Zou, Zhengyu Zhou, Jingyuan Xu, Weiwei Liu

AAAI 2024 A Closer Look at Curriculum Adversarial Training: From an Online Perspective Lianghe Shi, Weiwei Liu

ICML 2024 A Provable Decision Rule for Out-of-Distribution Detection Xinsong Ma, Xin Zou, Weiwei Liu

ICML 2024 A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks Boqi Li, Weiwei Liu

AAAI 2024 Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data Xin Zou, Weiwei Liu

AAAI 2024 DRF: Improving Certified Robustness via Distributional Robustness Framework Zekai Wang, Zhengyu Zhou, Weiwei Liu

NeurIPS 2024 Error Analysis of Spherically Constrained Least Squares Reformulation in Solving the Stackelberg Prediction Game Xiyuan Li, Weiwei Liu

CVPR 2024 LASIL: Learner-Aware Supervised Imitation Learning for Long-Term Microscopic Traffic Simulation Ke Guo, Zhenwei Miao, Wei Jing, Weiwei Liu, Weizi Li, Dayang Hao, Jia Pan

ICML 2024 Sequential Kernel Goodness-of-Fit Testing Zhengyu Zhou, Weiwei Liu

NeurIPS 2024 The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems Xiyuan Li, Youjun Wang, Weiwei Liu

IJCAI 2024 Zero-Shot Learning for Preclinical Drug Screening Kun Li, Weiwei Liu, Yong Luo, Xiantao Cai, Jia Wu, Wenbin Hu

NeurIPS 2023 A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications Yanbo Chen, Weiwei Liu

NeurIPS 2023 Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation Lianghe Shi, Weiwei Liu

ICML 2023 Better Diffusion Models Further Improve Adversarial Training Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, Shuicheng Yan

NeurIPS 2023 Characterization of Overfitting in Robust Multiclass Classification Jingyuan Xu, Weiwei Liu

ICML 2023 DDGR: Continual Learning with Deep Diffusion-Based Generative Replay Rui Gao, Weiwei Liu

IJCAI 2023 Deep Partial Multi-Label Learning with Graph Disambiguation Haobo Wang, Shisong Yang, Gengyu Lyu, Weiwei Liu, Tianlei Hu, Ke Chen, Songhe Feng, Gang Chen

ICML 2023 Delving into Noisy Label Detection with Clean Data Chenglin Yu, Xinsong Ma, Weiwei Liu

JMLR 2023 Generalization Bounds for Adversarial Contrastive Learning Xin Zou, Weiwei Liu

COLT 2023 Improved Bounds for Multi-Task Learning with Trace Norm Regularization Weiwei Liu

NeurIPS 2023 On the Adversarial Robustness of Out-of-Distribution Generalization Models Xin Zou, Weiwei Liu

JMLR 2023 RVCL: Evaluating the Robustness of Contrastive Learning via Verification Zekai Wang, Weiwei Liu

JMLR 2023 Sample Complexity for Distributionally Robust Learning Under Chi-Square Divergence Zhengyu Zhou, Weiwei Liu

CoRL 2023 TraCo: Learning Virtual Traffic Coordinator for Cooperation with Multi-Agent Reinforcement Learning Weiwei Liu, Wei Jing, Lingping Gao, Ke Guo, Gang Xu, Yong Liu

AAAI 2023 WAT: Improve the Worst-Class Robustness in Adversarial Training Boqi Li, Weiwei Liu

NeurIPS 2022 Defending Against Adversarial Attacks via Neural Dynamic System Xiyuan Li, Zou Xin, Weiwei Liu

NeurIPS 2022 On Robust Multiclass Learnability Jingyuan Xu, Weiwei Liu

NeurIPS 2022 On the Tradeoff Between Robustness and Fairness Xinsong Ma, Zekai Wang, Weiwei Liu

ICML 2022 Robustness Verification for Contrastive Learning Zekai Wang, Weiwei Liu

ICML 2020 Adaptive Adversarial Multi-Task Representation Learning Yuren Mao, Weiwei Liu, Xuemin Lin

IJCAI 2020 Collaboration Based Multi-Label Propagation for Fraud Detection Haobo Wang, Zhao Li, Jiaming Huang, Pengrui Hui, Weiwei Liu, Tianlei Hu, Gang Chen

AAAI 2020 Incorporating Label Embedding and Feature Augmentation for Multi-Dimensional Classification Haobo Wang, Chen Chen, Weiwei Liu, Ke Chen, Tianlei Hu, Gang Chen

IJCAI 2020 Learning from Multi-Dimensional Partial Labels Haobo Wang, Weiwei Liu, Yang Zhao, Tianlei Hu, Ke Chen, Gang Chen

IJCAI 2020 Multichannel Color Image Denoising via Weighted Schatten P-Norm Minimization Xinjian Huang, Bo Du, Weiwei Liu

ECML-PKDD 2020 Online Partial Label Learning Haobo Wang, Yuzhou Qiang, Chen Chen, Weiwei Liu, Tianlei Hu, Zhao Li, Gang Chen

IJCAI 2020 Opinion Maximization in Social Trust Networks Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu

AAAI 2020 Temporal Network Embedding with High-Order Nonlinear Information Zhenyu Qiu, Wenbin Hu, Jia Wu, Weiwei Liu, Bo Du, Xiaohua Jia

NeurIPS 2019 Copula Multi-Label Learning Weiwei Liu

IJCAI 2019 Discriminative and Correlative Partial Multi-Label Learning Haobo Wang, Weiwei Liu, Yang Zhao, Chen Zhang, Tianlei Hu, Gang Chen

ICML 2019 Sparse Extreme Multi-Label Learning with Oracle Property Weiwei Liu, Xiaobo Shen

AAAI 2019 Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification Chen Chen, Haobo Wang, Weiwei Liu, Xingyuan Zhao, Tianlei Hu, Gang Chen

AAAI 2018 Compact Multi-Label Learning Xiaobo Shen, Weiwei Liu, Ivor W. Tsang, Quan-Sen Sun, Yew-Soon Ong

IJCAI 2018 Deep Discrete Prototype Multilabel Learning Xiaobo Shen, Weiwei Liu, Yong Luo, Yew-Soon Ong, Ivor W. Tsang

IJCAI 2018 Discrete Network Embedding Xiaobo Shen, Shirui Pan, Weiwei Liu, Yew-Soon Ong, Quan-Sen Sun

AAAI 2018 Doubly Approximate Nearest Neighbor Classification Weiwei Liu, Zhuanghua Liu, Ivor W. Tsang, Wenjie Zhang, Xuemin Lin

IJCAI 2018 Ranking Preserving Nonnegative Matrix Factorization Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi

JMLR 2017 An Easy-to-Hard Learning Paradigm for Multiple Classes and Multiple Labels Weiwei Liu, Ivor W. Tsang, Klaus-Robert Müller

AAAI 2017 Compressed K-Means for Large-Scale Clustering Xiao-Bo Shen, Weiwei Liu, Ivor W. Tsang, Fumin Shen, Quan-Sen Sun

JMLR 2017 Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions Weiwei Liu, Ivor W. Tsang

NeurIPS 2017 Sparse Embedded $k$-Means Clustering Weiwei Liu, Xiaobo Shen, Ivor Tsang

AAAI 2016 Sparse Perceptron Decision Tree for Millions of Dimensions Weiwei Liu, Ivor W. Tsang

AAAI 2015 Effectively Predicting Whether and When a Topic Will Become Prevalent in a Social Network Weiwei Liu, Zhi-Hong Deng, Xiuwen Gong, Frank Jiang, Ivor W. Tsang

AAAI 2015 Large Margin Metric Learning for Multi-Label Prediction Weiwei Liu, Ivor W. Tsang

NeurIPS 2015 On the Optimality of Classifier Chain for Multi-Label Classification Weiwei Liu, Ivor Tsang