Li Shen's Homepage - Publication

Conference (# corresponding author, * equal contribution)

Weishi Li, Yong Peng, Mengyao Du, Fuhui Sun, Xiaoyan Wang, Li Shen, Hypernetwork aggregation for decentralized personalized federated learning, IJCAI, 2025.
Zixuan Hu, Yongxian Wei, Li Shen#, Chun Yuan, Dacheng Tao, Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs, CVPR, 2025.
Tao Sun, Yuhao Huang, Li Shen, Kele Xu, Bao Wang, Investigating the Role of Weight Decay in Enhancing Nonconvex SGD, CVPR, 2025.
Yingqi Liu, Qinglun Li, Jie Tan, Yifan Shi, Li Shen#, Xiaochun Cao, Understanding the Stability-based Generalization of Personalized Federated Learning, ICLR, 2025.
Jinluan Yang, Anke Tang, Didi Zhu, Zhengyu Chen, Li Shen#, Fei Wu#, Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace, ICLR, 2025.
Puning Zhao, Jiafei Wu, Zhe Liu, Li Shen, Zhikun Zhang, Rongfei Fan, Le Sun, Qingming Li, Enhancing Learning with Label Differential Privacy by Vector Approximation, ICLR, 2025. (spotlight)
Liang Chen, Li Shen#, Yang Deng, Xiaoyan Zhao, Bin Liang, Kam-Fai Wong#, PEARL: Towards Permutation-Resilient LLMs, ICLR, 2025.
Ziqing Fan, Siyuan Du, Shengchao Hu, Pingjie Wang, Li Shen#, Ya Zhang, Dacheng Tao, Yanfeng Wang#, Combatting Dimensional Collapse in LLM Pre-Training Data via Submodular File Selection, ICLR, 2025. (oral)
Qixin Zhang, Zongqi Wan, Yu Yang, Li Shen#, Dacheng Tao#, Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency, ICLR, 2025. (spotlight)
Siyu Luan, Zhenyi Wang, Li Shen, Zonghua Gu, Chao Wu, Dacheng Tao, Dynamic Neural Fortresses: An Adaptive Shield for Model Extraction Defense, ICLR, 2025.
Yongxian Wei, Zixuan Hu, Li Shen#, Zhenyi Wang, Chun Yuan#, Dacheng Tao, Open-Vocabulary Customization from CLIP via Data-Free Knowledge Distillation, ICLR, 2025. (oral)
Wenbin Wang, Liang Ding, Minyan Zeng, Xiabin Zhou, Li Shen, Yong Luo, Dacheng Tao, Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models, AAAI, 2025.
Junbao Zhuo, Shuhui Wang, Zhenghan Chen, Li Shen, Qingming Huang, Huimin Ma, Image-to-video Adaptation with Outlier Modeling and Robust Self-learning, AAAI, 2025.
Shengchao Hu, Wanru Zhao, Weixiong Lin, Li Shen#, Ya Zhang, Dacheng Tao, Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization, AAMAS, 2025.
Yan Sun, Li Shen#, Dacheng Tao, A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Need, NeurIPS, 2024. (spotlight)
Rui Min, Zeyu Qin, Nevin L. Zhang, Li Shen#, Minhao Cheng#, Uncovering, Explaining, and Mitigating the Superficial Safety of Backdoor Defense, NeurIPS, 2024. (spotlight)
Hongling Zheng, Li Shen#, Yong Luo#, Tongliang Liu, Jialie Shen, Dacheng Tao, Multi-Task Prompt Decision Transformer for Efficient Unseen Task Generalization, NeurIPS, 2024.
Yang Dai, Oubo Ma, Longfei Zhang, Xingxing Liang#, Shengchao Hu, Mengzhu Wang, Shouling Ji, Jincai Huang, Li Shen#, Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?, NeurIPS, 2024.
Puning Zhao, Lifeng Lai, Li Shen, Qingming Li, Jiafei Wu, Zhe Liu, A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy, NeurIPS, 2024.
Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, AJAY KUMAR JAISWAL, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu, Is C4 Dataset Enough for Pruning? An Investigation of Calibration Data for LLM Pruning, EMNLP Main, 2024.
Wenbin Wang, Liang Ding, Li Shen, Yong Luo, Han Hu, Dacheng Tao, WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge, ACM MM, 2024.
Zhiwei Hao, Zhongyu Xiao, Yong Luo, Jianyuan Guo, Jing Wang, Li Shen, Han Hu, PrimKD: Primary Modality Guided Multimodal Fusion for RGB-D Semantic Segmentation, ACM MM, 2024.
Zhenyi Wang, Li Shen#, Junfeng Guo, Tiehang Duan, Siyu Luan, Tongliang Liu, Mingchen Gao, Training A Secure Model against Data-Free Model Extraction, ECCV, 2024.
Boan Liu, Liang Ding, Li Shen, Keqin Peng, Yu Cao, Dazhao Cheng, Dacheng Tao, Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer, ECAI, 2024.
Peng Wang, Li Shen#, Zerui Tao, Shuaida He, Dacheng Tao, Generalization Analysis of Stochastic Weight Averaging with General Sampling, ICML, 2024.
Anke Tang*, Li Shen*, Yong Luo, Nan Yin, Lefei Zhang, Dacheng Tao, Merging Multi-Task Models via Weight-Ensembling Mixture of Experts, ICML, 2024.
Enneng Yang, Li Shen#, Zhenyi Wang#, Guibing Guo#, Xiaojun Chen, Xingwei Wang, Dacheng Tao, Representation Surgery for Multi-Task Model Merging, ICML, 2024.
Shengchao Hu, Ziqing Fan, Chaoqin Huang, Li Shen#, Ya Zhang, Yanfeng Wang, Dacheng Tao, Q-learning Transformer for Offline Reinforcement Learning, ICML, 2024.
Shengchao Hu, Ziqing Fan, Li Shen#, Ya Zhang, Yanfeng Wang, Dacheng Tao, HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning, ICML, 2024.
Zixuan Hu, Yongxian Wei, Li Shen#, Zhenyi Wang, Lei Li, Chun Yuan#, Dacheng Tao, Sparse Model Inversion: Efficient Inversion of Vision Transformers with Less Hallucination, ICML, 2024.
Yongxian Wei, Zixuan Hu, Li Shen#, Zhenyi Wang#, Yu Li, Chun Yuan#, Dacheng Tao, Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models, ICML, 2024.
Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu, Bo Du, Dacheng Tao, Revisiting Knowledge Distillation for Autoregressive Language Models, ACL, 2024.
Shuai Wang, Liang Ding, Li Shen, Yong Luo, Bo Du, Dacheng Tao, OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models, ACL Findings, 2024.
Kanxue Li, Baosheng Yu, Qi Zheng, Yibing Zhan, Yuhui Zhang, Tianle Zhang, Yijun Yang, Yue Chen, Lei Sun, Qiong Cao, Li Shen, Lusong Li, Dapeng Tao, Xiaodong He, MuEP: A Multimodal Benchmark for Embodied Planning with Foundation Models, IJCAI, 2024.
Yingqi Liu, Yifan Shi, Qinglun Li, Baoyuan Wu, Xueqian Wang, Li Shen#, Directed Decentralized Collaboration for Personalized Federated Learning, CVPR, 2024.
Zhiyuan Yu, Li Shen#, Liang Ding, Xinmei Tian, Yixin Chen, Dacheng Tao, Sheared Backpropagation for Finetuning Foundation Models, CVPR, 2024.
Ziming Hong, Li Shen, Tongliang Liu, Your Transferability Barrier is Fragile: Free-Lunch for Transferring the Non-Transferable Learning, CVPR, 2024. (Poster Highlight)
Yongxian Wei, Zixuan Hu, Zhenyi Wang, Li Shen#, Chun Yuan#, Dacheng Tao, FREE: Faster and Better Data-Free Meta-Learning, CVPR, 2024.
Yijun Yang, Tianyi Zhou, Kanxue Li, Dapeng Tao, Lusong Li, Li Shen#, Xiaodong He, Jing Jiang, Yuhui Shi#, Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld, CVPR, 2024. [MIT科技评论]
Jiayi Guan*, Li Shen*, Ao Zhou, Lusong Li, Han Hu, Xiaodong He, Guang Chen, Changjun Jiang, POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement Learning, CVPR, 2024.
Enneng Yang, Zhenyi Wang#, Li Shen#, Shiwei Liu, Guibing Guo#, Xingwei Wang, Dacheng Tao, AdaMerging: Adaptive Model Merging for Multi-Task Learning, ICLR, 2024.
Anke Tang, Li Shen#, Yong Luo#, Yibing Zhan, Han Hu, Bo Du, Yixin Chen, Dacheng Tao, Parameter-Efficient Multi-Task Model Fusion with Partial Linearization, ICLR, 2024.
Shengchao Hu, Li Shen#, Ya Zhang, Dacheng Tao, Learning Multi-Agent Communication from Graph Modeling Perspective, ICLR, 2024.
Guozheng Ma, Lu Li, Sen Zhang, Zixuan Liu, Zhen Wang, Yixin Chen, Li Shen#, Xueqian Wang#, Dacheng Tao, Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages, ICLR, 2024.
Zhenyi Wang, Yan Li, Li Shen, Heng Huang, A Unified and General Framework for Continual Learning, ICLR, 2024.
Ziming Hong, Zhenyi Wang, Li Shen, Yu Yao, Zhuo Huang, Shiming Chen, Chuanwu Yang, Mingming Gong, Tongliang Liu, Improving Non-Transferable Representation Learning by Harnessing Content and Style, ICLR, 2024. (spotlight)
Nan Yin, Mengzhu Wang, Zhenghan Chen, Li Shen, Huan Xiong, Bin Gu, Xiao Luo, DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption, ICLR, 2024.
Di Wu, Yuling Jiao, Li Shen, Haizhao Yang, Xiliang Lu, Neural Network Approximation for Pessimistic Offline Reinforcement Learning, AAAI, 2024.
Yan Sun, Li Shen#, Dacheng Tao, Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization, NeurIPS, 2023.
Miaoxi Zhu, Li Shen#, Bo Du#, Dacheng Tao, Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm, NeurIPS, 2023.
Zhenyi Wang#, Li Shen#, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, David Doermann, Mingchen Gao#, Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training, NeurIPS, 2023.
Enneng Yang, Li Shen#, Zhenyi Wang#, Tongliang Liu, Guibing Guo#, An Efficient Dataset Condensation Plugin and Its Application to Continual Learning, NeurIPS, 2023.
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.
Xuming An, Li Shen#, Han Hu#, Yong Luo, Federated Learning with Manifold Regularization and Normalized Update Reaggregation, NeurIPS, 2023.
Rui Min, Zeyu Qin, Li Shen, Minhao Cheng, Stable Backdoor Purification with Feature Shift Tuning, NeurIPS, 2023.
Guozheng Ma, Linrui Zhang, Haoyu Wang, Lu Li, Zilin Wang, Zhen Wang, Li Shen#, Xueqian Wang#, Dacheng Tao, Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning, NeurIPS, 2023.
Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu, Dynamic Sparsity Is Channel-Level Sparsity Learner, NeurIPS, 2023.
Miaoxi Zhu, Qihuang Zhong, Li Shen, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao, Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models, EMNLP Main, 2023.
Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao, Merging Experts into One: Improving Computational Efficiency of Mixture of Experts, EMNLP Main, 2023. (oral)
keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, Dacheng Tao, Towards Making the Most of ChatGPT for Machine Translation, EMNLP Findings, 2023.
Guanyu Xu, Jiawei Hao, Li Shen, Han Hu, Yong Luo, Hui Lin, Jialie Shen, LGViT: Dynamic Early Exiting for Accelerating Vision Transformer, ACM MM, 2023.
Enneng Yang, Li Shen#, Zhenyi Wang#, Shiwei Liu, Guibing Guo#, Xingwei Wang, Data Augmented Flatness-aware Gradient Projection for Continual Learning, ICCV, 2023.
Yaopei Zeng, Lei Liu, Li Liu, Li Shen, Shaoguo Liu, Baoyuan Wu, Global Balanced Experts for Federated Long-tailed Learning, ICCV, 2023.
Mingli Zhu, Shaokui Wei, Li Shen, Yanbo Fan, Baoyuan Wu, Enhancing Fine-Tuning based Backdoor Defense with Sharpness-Aware Minimization, ICCV, 2023.
Yan Sun, Li Shen#, Shixiang Chen, Liang Ding, Dacheng Tao, Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape, ICML, 2023. (oral)
Zixuan Hu, Li Shen#, Zhenyi Wang, Baoyuan Wu, Chun Yuan#, Dacheng Tao, Learning to Learn from APIs: Black-box Data-free Meta-Learning, ICML, 2023.
Yifan Shi, Li Shen#, Kang Wei, Yan Sun, Bo Yuan, Xueqian Wang#, Dacheng Tao, Improving the Model Consistency of Decentralized Federated Learning, ICML, 2023.
Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, Xiao Luo, CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification, ICML, 2023.
Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu, Are Large Kernels Better Teachers than Transformers for ConvNets?, ICML, 2023.
Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy, Enhancing Adversarial Training via Reweighting Optimization Trajectory, ECML, 2023.
Shenao Zhang, Li Shen, Lei Han, Li Shen, Learning Meta Representation for Agents in Multi-Agent Reinforcement Learning, CoLLAs, 2023. (oral)
Zixuan Hu, Li Shen#, Zhenyi Wang, Tongliang Liu, Chun Yuan#, Dacheng Tao, Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning, CVPR, 2023.
Zhenyi Wang, Li Shen#, Donglin Zhan, Qiuling Suo, Yanjun Zhu, Tiehang Duan, Mingchen Gao, MetaMix: Towards Corruption-Robust Continual Learning with Temporally Self-Adaptive Data Transformation, CVPR, 2023.
Yifan Shi, Yingqi Liu, Kang Wei, Li Shen#, Xueqian Wang#, Dacheng Tao, Make Landscape Flatter in Differentially Private Federated Learning, CVPR, 2023.
Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu, Robust Generalization against Corruptions via Worst-Case Sharpness Minimization, CVPR, 2023.
Yan Sun, Li Shen#, Tiansheng Huang, Liang Ding, Dacheng Tao, FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy, ICLR, 2023.
Runzhong Wang, Li Shen, Yiting Chen, Xiaokang Yang, Dacheng Tao, Junchi Yan, Relaxed Combinatorial Optimization Networks with Self-Supervision: Theoretical and Empirical Notes on the Cardinality-Constrained Case, ICLR, 2023.
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.
Zhihao Cheng, Li Shen, Dacheng Tao, Off-policy Imitation Learning from Visual Inputs, ICRA, 2023. [paper]
Jing Dong, Li Shen, Yinggan Xu, Baoxiang Wang, Provably Efficient Convergence of Actor Critic with Nonlinear Function Approximation and Adaptive Gradients, AAMAS, 2023.
Linrui Zhang, Qin Zhang, Li Shen#, Bo Yuan, Xueqian Wang#, Dacheng Tao, Evaluating Model-free Reinforcement Learning toward Safety-Critical Tasks, AAAI, 2023.
Zhihao Chen, Kaining Zhang, Li Shen, Dacheng Tao, Offline Quantum Reinforcement Learning in A Conservative Manner, AAAI, 2023.
Dui Wang, Li Shen, Yong Luo, Han Hu, kehua Su, Yonggang Wen, Dacheng Tao, FedABC: Targeting Fair Competition in Personalized Federated Learning, AAAI, 2023.
Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu, Li Shen, Xihua Chen, Lei Xiao, Jie Jiang, Guibing Guo, AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning, AAAI, 2023.
Qihuang Zhong, Liang Ding, Li Shen, Peng Mi, Juhua Liu, Bo Du, Dacheng Tao, Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models, Findings of EMNLP, 2022.
Peng Mi, Li Shen#, Tianhe Ren, Yiyi Zhou, Xiaoshuai Sun, Rongrong Ji, Dacheng Tao, Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach, NeurIPS, 2022.
Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell, MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models, NeurIPS, 2022.
Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, Baoyuan Wu, Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation, NeurIPS, 2022.
Zhenyi Wang, Li Shen#, Le Fang, Qiuling Suo, Donglin Zhan, Tiehang Duan, Mingchen Gao, Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions, ECCV, 2022.
Linrui Zhang, Zichen Yan, Li Shen, Shoujie Li, Xueqian Wang, Dacheng Tao, Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning, IROS, 2022.
Nan Yin, Li Shen, Baopu Li, Mengzhu Wang, Xiao Luo, Chong Chen, Zhigang Luo, Xian-Sheng Hua, DEAL: An Unsupervised Domain Adaptive Framework for Graph-level Classification, ACM MM, 2022.
Changtong Zan, Liang Ding, Li Shen, Yu Cao, Weifeng Liu, Dacheng Tao, On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation, COLING, 2022.
Zhenyi Wang, Xiaoyang Wang, Li Shen, Qiuling Suo, Kaiqiang Song, Dong Yu, Yan Shen, Mingchen Gao, Meta-Learning without Data via Wasserstein Distributionally-Robust Model Fusion, UAI, 2022.
Zhenyi Wang#, Li Shen#, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao#, Improving Task-free Continual Learning by Distributionally Robust Memory Evolution, ICML, 2022.
Rong Dai, Li Shen#, Fengxiang He, Xinmei Tian#, Dacheng Tao, DisPFL: Towards Communication-Efficient Personalized Federated learning via Decentralized Sparse Training, ICML, 2022.
Chang Liu, Chenfei Lou, Runzhong Wang, Alan Yuhan Xi, Li Shen, Junchi Yan, Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning, ICML, 2022.
Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang liu, Understanding Robust Overfitting of Adversarial Training and Beyond, ICML, 2022.
Linrui zhang, Li Shen#, Long Yang, Shixiang Chen, Bo Yuan, Xueqian Wang, Dacheng Tao, Penalized Proximal Policy Optimization for Safe Reinforcement Learning, IJCAI, 2022. [full paper]
Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu, Robust Weight Perturbation for Adversarial Training, IJCAI, 2022.
Zhenyi Wang, Li Shen, Tiehang Duan, Donglin Zhan, Le Fang, Mingchen Gao, Learning to Learn and Remember Super Long Multi-Domain Task Sequence, CVPR, 2022. (oral)
Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, Lingyu Duan, Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning, CVPR, 2022.
Yang Sun, Fajie Yuan, Min Yang, Alexandros Karatzoglou, Li Shen, Xiaoyan Zhao, Enhancing Top-N Item Recommendations by Peer Collaboration, SIGIR, 2022.
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training, ICLR, 2022.
Shaopeng Fu, Fengxiang He, Yang Liu, Li Shen, Dacheng Tao, Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning, ICLR, 2022.
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu, Sparse Training via Boosting Pruning Plasticity with Neuroregeneration, NeurIPS, 2021.
Yinghua Gao, Li Shen, Shu-Tao Xia, DAG-GAN: Causal Structure Learning with Generative Adversarial Nets, IEEE ICASSP, 2021.
Congliang Chen, Jiawei Zhang, Li Shen#, Peilin Zhao, Zhi-Quan Luo, Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization, AISTATS, 2021.
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang, Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks, ICML, 2020.
Ganzhao Yuan, Li Shen, Wei-Shi Zheng, A Block Decomposition Algorithm for Sparse Optimization, KDD, 2020.
Yingru Liu, Xuewen Yang, Dongliang Xie, Xin Wang, Li Shen, Haozhi Huang, Niranjan Balasubramanian, Adaptive Activation Network and Functional Regularization for Efficient Deep Multi-Task Learning, AAAI, 2020.
Guibing Guo, Enneng Yang#, Li Shen#, Xiaochun Yang, Xiaodong He, Discrete Trust-aware Matrix Factorization for Fast Recommendation, IJCAI, 2019.
Fangyu Zou*, Li Shen*, Zequn Jie, Weizhong Zhang, Wei Liu, A Sufficient Condition for Convergences of Adam and RMSProp, CVPR, 2019. (oral)
Ganzhao Yuan, Li Shen, Wei-Shi Zheng, A Decomposition Algorithm for Sparse Generalized Eigenvalue Problem, CVPR, 2019.
Li Shen, Peng Sun, Yitong Wang, Wei Liu, Tong Zhang, An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method, ICML, 2018.
Li Shen, Wei Liu, Ganzhao Yuan, Shiqian Ma, GSOS: Gauss-Seidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization, ICML, 2017. [extended version]
Li Shen, Wei Liu, Junzhou Huang, Yu-Gang Jiang, Shiqian Ma, Adaptive Proximal Average Approximation for Composite Convex Minimization, AAAI, 2017. [appendix]

Journal (# corresponding author, * equal contribution)

Ziyi Zhang, Sen Zhang, Li Shen, Yibing Zhan, Yong Luo, Han Hu, Bo Du, Yonggang Wen, Dacheng Tao, Aligning Text-to-Image Diffusion Models with Constrained Reinforcement Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Tianshuo Xu, Xiaoshuai Sun, Tongliang Liu, Rongrong Ji, Dacheng Tao, Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Xingrun Yan, Shiyuan Zuo, Rongfei Fan, Han Hu, Li Shen, Puning Zhao, Yong Luo, Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets, IEEE Transactions on Mobile Computing, 2025.
Xiaoge Deng, Li Shen, Shengwei Li, Tao Sun, Dongsheng Li, Dacheng Tao, Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Qinglun Li, Miao Zhang, Nan Yin, Quanjun Yin#, Li Shen#, Xiaochun Cao, Asymmetrically Decentralized Federated Learning, IEEE Transactions on Computers, 2025
JD Explore Academy-Foundation Model Team: Chao Xue, Wei Liu, Shuai Xie, Zhenfang Wang, Jiaxing Li, Xuyang Peng, Liang Ding, Shanshan Zhao, Qiong Cao, Yibo Yang, Fengxiang He, Bohua Cai, Rongcheng Bian, Yiyan Zhao, Heliang Zheng, Xiangyang Liu, Dongkai Liu, Daqing Liu, Li Shen, Chang Li, Shijin Zhang, Yukang Zhang, Guanpu Chen, Shixiang Chen, Yibing Zhan, Jing Zhang, Chaoyue Wang, and Dacheng Tao, OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System, npj Artificial Intelligence, 2025. (Nature Partner Journal)
Guanyu Xu, Jiawei Hao, Yong Luo, Li Shen, Han Hu, Dan Zeng, EEformer: Early Exiting for Transformer with Global-Local Exits and Progressive Fine-Tuning, IEEE Transactions on Multimedia, 2025.
Tao Sun, Li Shen, Xinwang Liu, On Nonconvex SGD under Unbounded Noise with Weak Gradient Lipschitz and Delayed Stochastic Gradient, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Zhiwei Hao, Jianyuan Guo, Li Shen, Yong Luo, Han Hu, Yonggang Wen, ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning, International Journal of Computer Vision, 2025.
Yifei Cheng, Li Shen#, Linli Xu, Xun Qian, Shiwei Wu, Yiming Zhou, Tie Zhang, Dacheng Tao, Enhong Chen, Communication-Efficient Distributed Learning with Local Immediate Error Compensation, Neural Networks, 2025.
Guozhi Liu, Weiwei Lin, Tiansheng Huang, Fang Shi, Wentai Wu, Li Shen, AdaptiveFL: Communication-Adaptive Federated Learning under Dynamic Bandwidth, IEEE Transactions on Neural Networks and Learning Systems, 2025.
Shuang Wu, Shixiang Chen, Li Shen, Lefei Zhang, Dacheng Tao, Constraint Boundary Wandering Framework: Enhancing Constrained Optimization with Deep Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Anke Tang, Li Shen#, Yong Luo#, Shiwei Liu, Han Hu, Bo Do, Dacheng Tao, Data-Adaptive Weight-Ensembling for Multi-Task Model Fusion, International Journal of Computer Vision, 2025.
Shengchao Hu, Li Shen#, Ya Zhang, Dacheng Tao, Graph Decision Transformer for Offline Reinforcement Learning, SCIENCE CHINA Information Sciences, 2025.
Qinglun Li, Li Shen#, Guanghao Li, Quanjun Yin#, Dacheng Tao, DFedADMM: Dual Constraint Controlled Model Inconsistency for Decentralize Federated Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Junyu Luo, Yusheng Zhao, Xiao Luo, Zhiping Xiao, Wei Ju, Li Shen, Dacheng Tao, Ming Zhang, Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval, IEEE Transactions on Image Processing, 2025.
Haoyu Wang, Guozheng Ma, Cong Yu, Ning Gui, Linrui Zhang, Zhiqi Huang, Suwei Ma, Yongzhe Chang, Sen Zhang, Li Shen#, Xueqian Wang#, Peilin Zhao, Dacheng Tao, Are Large Language Models Really Robust to Word-Level Perturbations? Transactions on Machine Learning Research, 2025.
Yilun Kong, Hangyu Mao, Qi Zhao, Bin Zhang, Jingqing Ruan, Li Shen, Yongzhe Chang, Xueqian Wang, Rui Zhao, Dacheng Tao, QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning, Transactions on Machine Learning Research, 2025.
Enneng Yang, Li Shen#, Zhenyi Wang, Shiwei Liu, Guibing Guo#, Xingwei Wang, Dacheng Tao, Revisiting Flatness-aware Optimization in Continual Learning with Orthogonal Gradient Projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Mengzhu Wang, Houcheng Su, Sijia Wang, Shanshan Wang, Nan Yin, Li Shen, Long Lan, Liang Yang, Xiaochun Cao, Graph Convolutional Mixture-of-Experts Learner Network for Long-Tailed Domain Generalization, IEEE Transactions on Circuits and Systems for Video Technology, 2025.
Qiuyu Huang, Zequn Jie, Lin Ma, Li Shen#, Shenqi Lai#, A Pyramid Fusion MLP for Dense Prediction, IEEE Transactions on Image Processing, 2024.
Hongling Zheng, Li Shen#, Anke Tang, Yong Luo#, Han Hu#, Bo Du#, Yonggang Wen, Dacheng Tao, Learning from models beyond fine-tuning, Nature Machine Intelligence, 2025.
Qinglun Li, Miao Zhang, Tao Sun, Quanjun Yin#, Li Shen#, DFedGFM: Pursuing Global Consistency for Decentralized Federated Learning via Global Flatness and Global Momentum, Neural Networks, 2024.
Changtong Zan, Liang Ding, Li Shen, Yu Cao, Weifeng Liu, Code-switching finetuning: Bridging multilingual pretrained language models for enhanced cross-lingual performance, Engineering Applications of Artificial Intelligence, 2024.
Yuesong Tian, Li Shen, Xiang Tian, Dacheng Tao, Zhifeng Li, Wei Liu, Yaowu Chen, DGL-GAN: Discriminator Guided GAN Compression, The Visual Computer, 2024.
Haibin Zhou, Tong Wei, Zichuan Lin, Junyou Li, Junliang Xing, Yuanchun Shi, Li Shen, Chao Yu, Deheng Ye, Revisiting Discrete Soft Actor-Critic, Transactions on Machine Learning Research, 2024.
Zhenyi Wang#, Enneng Yang, Li Shen#, Heng Huang, A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
Li Shen*, Yan Sun*, Zhiyuan Yu*, Liang Ding, Xinmei Tian, Dacheng Tao, On Efficient Training of Large-Scale Deep Learning Models, ACM Computing Surveys, 2024. [新智元]
Enneng Yang, Zhenyi Wang, Li Shen#, Nan Yin, Tongliang Liu, Guibing Guo#, Xingwei Wang, Dacheng Tao, Continual Learning From a Stream of APIs, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
Yujia Gao, Li Shen, Liang Liu, Zijian Cao, Dacheng Tao, Huadong Ma, Nei Kato, Neural-aware Decoupling Fusion based Personalized Federated Learning for Intelligent Sensing, ACM Transactions on Sensor Networks, 2024.
Fei Wang, Liang Ding, Jun Rao, Ye Liu, Li Shen, Changxing Ding, Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining? ACM Transactions on Multimedia Computing Communications and Applications, 2024.
Nan Yin, Yawei Zhao, Li Shen, Wei Ju, Chong Chen, Xian-Sheng Hua, Xiao Luo, SPORT: A Subgraph Perspective on Graph Classification with Label Noise, ACM Transactions on Knowledge Discovery from Data, 2024.
Yongxian Wei, Zixuan Hu, Li Shen#, Zhenyi Wang, Lei Li, Yu Li, Chun Yuan#, Meta-Learning without Data via Unconditional Diffusion Models, IEEE Transactions on Circuits and Systems for Video Technology, 2024.
Shengchao Hu, Li Shen#, Ya Zhang, Yixin Chen, Dacheng Tao, On Transforming Reinforcement Learning by Transformer: The Development Trajectory, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
Xuming An, Dui Wang, Li Shen, Yong Luo, Han Hu, Bo Du, Yonggang Wen, Dacheng Tao, Federated Learning with Only Positive Labels by Exploring Label Correlations, IEEE Transactions on Neural Networks and Learning Systems, 2024.
Tiehang Duan, Zhenyi Wang, Li Shen, Gianfranco Doretto, Donald Adjeroh, Fang Li, Cui Tao, Retain and Adapt: Online Sequential EEG Classification with Subject Shift, IEEE Transactions on Artificial Intelligence, 2024.
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, International Journal of Computer Vision, 2024.
Derun Song, Enneng Yang, Guibing Guo, Li Shen, Linying Jiang, Xingwei Wang, Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation System, ACM Transactions on Knowledge Discovery from Data, 2024.
Yifan Shi, Kang Wei, Li Shen, Jun Li, Xueqian Wang, Bo Yuan, Song Guo, Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing, IEEE Transactions on Mobile Computing, 2024.
Guanghao Li, Wansen Wu, Yan Sun, Li Shen#, Baoyuan Wu, Dacheng Tao, Visual Prompt Based Personalized Federated Learning, Transactions on Machine Learning Research, 2023.
Yuesong Tian, Li Shen, Xiang Tian, Zhifeng Li, Yaowu Chen, Dynamic PDGAN: Discriminator-boosted Knowledge Distillation for StyleGANs, Journal of Electronic Imaging, 2023.
Nan Yin*, Li Shen*, Huan Xiong, Bin Gu, Chong Chen, Xian-Sheng Hua, Siwei Liu, Xiao Luo, Messages Are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-series Forecasting, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
Hao Sun, Li Shen#, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao, AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks, Neural Networks, 2023.
Tianfu Wang, Li Shen#, Qilin Fan, Tong Xu, Tongliang Liu, Hui Xiong#, Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning, IEEE Transactions on Services Computing, 2023.
Zhenyi Wang#, Li Shen#, Tiehang Duan, Qiuling Suo, Le Fang, Wei Liu, Mingchen Gao, Distributionally Robust Memory Evolution with Generalized Divergence for Continual Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
Congliang Chen, Li Shen#, Wei Liu, Zhi-Quan Luo, Efficient-Adam: Communication-Efficient Distributed Adam, IEEE Transactions on Signal Processing, 2023.
Jianbo Wang, Haozhi Huang, Li Shen, Xuan Wang, Toshihiko Yamasaki, Hierarchical Detailed Intermediate Supervision for Image-to-Image Translation, IEICE Transactions on Information and Systems, 2023.
Hanchi Huang, Li Shen#, Deheng Ye, Wei Liu, Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints, IEEE Transactions on Neural Networks and Learning Systems, 2023.
Rong Dai, Xun Yang#, Yan Sun, Li Shen#, Xinmei Tian, Meng Wang, and Yongdong Zhang, FedGAMMA: Federated Learning with Global Sharpness Aware Minimization, IEEE Transactions on Neural Networks and Learning Systems, 2023.
Yan Sun, Li Shen#, Hao Sun, Liang Ding, Dacheng Tao, Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
Zhihao Cheng, Li Shen, Miaoxi Zhu, Jiaxian Guo, Meng Fang, Liu Liu, Bo Du, Dacheng Tao, Prescribed Safety Performance Imitation Learning from A Single Expert Dataset, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
Shiwei Liu, Yuesong Tian, Tianlong Chen, Li Shen#, Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance, International Journal of Computer Vision, 2023.
Li Shen, Congliang Chen#, Fangyu Zou, Zequn Jie, Ju Sun, Wei Liu, A Unified Analysis of AdaGrad with Weighted Aggregation and Momentum Acceleration, IEEE Transactions on Neural Networks and Learning Systems, 2023.
Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao, Quantum Imitation Learning, IEEE Transactions on Neural Networks and Learning Systems, 2023.
Nan Yin, Li Shen, Mengzhu Wang, Xiao Luo, Zhigang Luo, Dacheng Tao, OMG: Towards Effective Graph Classification Against Label Noise, IEEE Transactions on Knowledge and Data Engineering, 2023.
Rui Xu, Zhi Liu, Yong Luo, Han Hu, Li Shen, Bo Du, Kaiming Kuang, Jiancheng Yang, SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023.
Tiansheng Huang, Li Shen#, Yan Sun, Weiwei Lin, Dacheng Tao, Fusion of Global and Local Knowledge for Personalized Federated Learning, Transactions on Machine Learning Research, 2023.
Zixuan Hu, Li Shen#, Shenqi Lai, Chun Yuan#, Task-adaptive Feature Disentanglement and Hallucination for Few-shot Classification, IEEE Transactions on Circuits and Systems for Video Technology, 2023.
Mengzhu Wang, Shanshan Wang, Wei Wang, Li Shen, Xiang Zhang, Long Lan, Zhigang Luo, Reducing Bi-level Feature Redundancy for Unsupervised Domain Adaptation, Pattern Recognition, 2023.
Jun Rao, Liang Ding, Shuhan Qi, Meng Fang, Yang Liu, Li Shen, Dacheng Tao, Dynamic Contrastive Distillation for Image-Text Retrieval, IEEE Transactions on Multimedia, 2023.
Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard Bondell, FedDAG: Federated DAG Structure Learning, Transactions on Machine Learning Research, 2022.
Hanchi Huang, Deheng Ye, Li Shen, Wei Liu, Curriculum-based Asymmetric Multi-task Reinforcement Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
Han Huang, Li Shen#, Chaoyang He, Weisheng Dong, Wei Liu, Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution, IEEE Transactions on Circuits and Systems for Video Technology, 2022.
Enneng Yang, Xin Xin, Li Shen, Yudong Luo, Guibing Guo, Generalized Embedding Machines for Recommender Systems, Machine Intelligence Research, 2022.
Luofeng Liao, Li Shen#, Jia Duan, Mladen Kolar, Dacheng Tao, Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems, Machine Learning, 2022.
Dongdong Liu, Seak-Weng Vong, Li Shen, Improved Uniqueness Conditions of Solution for Multilinear PageRank and Its Application, Linear and Multilinear Algebra, 2022.
Congliang Chen*, Li Shen*#, Fangyu Zou*, Wei Liu#, Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration, Journal of Machine Learning Research, 2022.
Chuang Zhang, Li Shen, Jian Yang, Chen Gong, Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels, Machine Learning, 2022.
Tiansheng Huang, Weiwei Lin, Li Shen, Keqin Li, Albert Y Zomaya, Stochastic Client Selection for Federated Learning with Volatile Clients, IEEE Internet of Things Journal, 2022.
Mengzhu Wang, Paul Li, Li Shen, Ye Wang, Shanshan Wang, Wei Wang, Xiang Zhang, Junyang Chen, Zhigang Luo, Informative Pairs Mining based Adaptive Metric Learning for Adversarial Domain Adaptation, Neural Networks, 2022.
Yuesong Tian, Li Shen#, Li Shen, Guinan Su, Zhifeng Li, Wei Liu#, AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
Congliang Chen, Li Shen#, Haozhi Huang, Wei Liu, Quantized Adam with Error Feedback, ACM Transactions on Intelligent Systems and Technology, 2021.
Meng Cao, Haozhi Huang, Hao Wang, Xuan Wang, Li Shen, Sheng Wang, Linchao Bao, Zhifeng Li, Jiebo Luo, UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing, IEEE Transactions on Image Processing, 2021.
Ye Tian, Meiling Chen, Li Shen#, Bo Jiang, Zhifeng Li, Knowledge Distillation with Multi-Objective Divergence Learning, IEEE Signal Processing Letters, 2021.
Xiaojun Chen, Weijun Hong, Feiping Nie, Joshua Zhexue Huang, Li Shen, Enhanced Balanced Min Cut, International Journal of Computer Vision, 2020.
Baoyuan Wu, Li Shen#, Tong Zhang, Bernard Ghanem, MAP Inference via L2-Sphere Linear Program Reformulation, International Journal of Computer Vision, 2020.
Li Shen#, Over Relaxed Hybrid Proximal Extra-gradient Algorithm and Its Application to Several Operator Splitting Methods, Journal of Mathematical Analysis and Applications, 2017.

Workshop (peer-reviewed)

Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin, Li Shen, Zhong Zhang, Bingzhe Wu, Liu Liu, Yatao Bian, Tingyang Xu, Xueqian Wang, Peilin Zhao, Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping, ICML 2024 Workshop on Models of Human Feedback for AI Alignment, 2024.
Qin Zhang, Linrui Zhang, Li Shen, Haoran Xu, Bowen Wang, Xueqian Wang, Bo Yuan, Yongzhe Chang, SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning, ICLR 2023 Autonomous Driving Workshop, 2023.
Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang, Di Fan, Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, Salman Avestimehr, FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks, International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI, 2022.
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr, FedML: A Research Library and Benchmark for Federated Machine Learning, NeurIPS Workshop on SpicyFL, 2020. [code][Best Paper Award]