Associate Professor, Ph.D. Advisor
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University
Address: Room 2-914, Engineering building, SYSU, Guangming, Shenzhen, Guang Dong, China
Email: shenli6@mail.sysu.edu.cn (Preferred)/ mathshenli@gmail.com
WeChat: mathshenli
Currently, Li Shen is an associate professor at the School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University. Previously, he was a research scientist at JD Explore Academy, Beijing, and a senior researcher at Tencent AI Lab, Shenzhen. He received his bachelor's degree and Ph.D. from the School of Mathematics, South China University of Technology. His research interests include theory and algorithms for nonsmooth convex and nonconvex optimization, and their applications in trustworthy artificial intelligence, deep learning, and reinforcement learning. He has published more than 100 papers in peer-reviewed top-tier journal papers (Nature Machine Intelligence, JMLR, IEEE TPAMI, IJCV, IEEE TKDE, IEEE TSP, IEEE TIP, etc.) and conference papers (ICML, NeurIPS, ICLR, CVPR, ICCV, etc.). He has served as the senior program committee for AAAI and area chairs for ICML, NeurIPS, ICLR, ACM MM, ACML, and ICPR. He has also served as the associate editor for IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Multimedia, and action editor for Neural Networks.
⭐️⭐️⭐️ Our team recruits several postdocs to work on trustworthy artificial intelligence, distributed/federated learning, continual learning, reinforcement learning, efficient training of foundation models, and large language models security. [逸仙博士后][深圳博士后政策][光明区人才政策]
⭐️⭐️⭐️ Our team recruits several self-motivated students (Ph.Ds., masters, undergraduate students, interns, gap year students) to work on continual learning, reinforcement learning, and LLMs/VLMs/MLLMs. In general, our team recruits Ph.D students mainly from the (remote and onsite) interns and RAs. If you are interested in my group, please contact me as soon as possible.
⭐️⭐️⭐️ I am always open to collaborating with remote students and visitors (at least 12 months) who are interested in optimization and machine learning.
2025.04: I was invited as an Area Chair for NeurIPS 2025.
2025.03: I was invited as an Associate Editor for IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE).
2025.03: One paper was accepted to IEEE TPAMI (learn to optimize).
2025.03: One paper was accepted to IJCV (model merging).
2025.03: One paper was accepted to SCIENCE CHINA Information Sciences (Graph Decision Transformer).
2025.03: I was invited as an Area Chair for ACMMM 2025.
2025.02: I was invited as an Associate Editor for IEEE Transactions on Multimedia (IEEE TMM).
2025.02: Two papers were accepted to CVPR (deep learning optimizer, data-free learning).
2025.02: One paper was accepted to IEEE TPAMI (Decentralized Federated Learning).
2025.02: One paper was accepted to IEEE TIP (Efficient retrieval).
2025.02: One paper was accepted to TMLR (robust analysis of LLM).
2025.02: One paper was accepted to TMLR (RL-based prompt optimization).
2025.01: One paper was accepted to IEEE TPAMI (continual learning).
2025.01: Eight papers (2*oral, 2*spotlight) were accepted to ICLR 2024 (data-free learning, trustworthy model merging, learning theory, robust LLM, efficient LLM training)
2025.01: I was invited as a senior program committee (SPC) for ECAI-2025.
2025.01: One paper was accepted to IEEE TCSVT (MoE).
2024.12: One paper was accepted to IEEE TIP (Vision Transformer).
2024.12: One paper was accepted to Neural Networks (Decentralized federated learning).
2024.12: One paper was accepted to AAMAS 2025 (Generative RL).
2024.12: Two papers were accepted to AAAI 2025 (multimodal learning).
2024.11: I was invited as an Area Chair for ICML 2025.
2024.11: One paper was accepted to Nature Machine Intelligence (Learn from model, Data free learning).
2024.11: One paper was accepted to TMLR (reinforcement learning).
2024.11: One paper was accepted to IEEE TPAMI (catastrophic forgetting).
2024.10: One paper was accepted to ACM Computing Surveys (efficient training)
2024.09: Five papers (2 spotlight, 3 poster) were accepted to NeurIPS 2024 (LMC for robustness, learning theory of FL, Generative RL, Differential Privacy)
2024.09: I was invited to join the editorial board (Action Editor) of Neural Networks (JCR Q1, CCF B, and IF=6).
2024.09: One paper was accepted to EMNLP 2024 (LLM Pruning).
2024.09: One paper was accepted to IEEE TPAMI (API learning).
2024.09: One paper was accepted to ACM TOSN (Personalized federated learning).
2024.08: One paper was accepted to ACM TOMM (VLM Pretraining).
2024.08: I was invited as an Area Chair for ICLR 2025.
2024.07: One paper was accepted to ACM TKDD (Lable Noise Learning).
2024.07: Two papers were accepted to ACMM MM 2024 (Multimodal Fusion).
2024.07: One paper was accepted to ECAI 2024 (orthogonal optimizer for MoE).
2024.07: One paper was accepted to ECCV 2024 (Data-Free Model Extraction).
2024.06: One paper was accepted to IEEE TCSVT (Synthetic Data with Diffusion Models).
2024.06: I was invited as a Senior Program Committee for AAAI 2025.
2024.05: One paper was accepted to IEEE TPAMI (Decision Transformer).
2024.05: I was invited as an Area Chair for ACML 2024.
2024.05: Two papers were accepted to ACL 2024 (rethinking KD for LM, LLM benchmark).
2024.05: Seven papers were accepted to ICML 2024 (Decision transformer, data-free learning, model fusion, learning theory)
2024.04: One paper was accepted to IEEE TNNLS (federated learning)
2024.04: One paper was accepted to IJCAI (Benchmark for embodied AI).
2024.03: One paper was accepted to IEEE TAI (DRO-based meta-learning for EEG Classification ).
2024.03: One paper was accepted to IJCV (sparse training for OOD Generalization).
2024.02: One paper was accepted to ACM TKDD (multitask learning).
2024.02: Six papers were accepted to CVPR 2024 (safe RL, embodied AI, data-free learning, trustworthy DFKD, memory-efficient optimizer, decentralized FL).
2024.02: One paper was accepted to IEEE TMC (sparse training for federated learning with differential privacy).
2024.01: I was invited as an Area Chair for ICML 2024.
2024.01: Seven papers were accepted to ICLR 2024 (reinforcement learning, model fusion, continual learning, and trustworthy ML).
2023.12: One paper was accepted to TMLR (visual prompt for personalized federated learning).
2023.12: One paper was accepted to Journal of Electronic Imaging (efficient training for large scale GAN).
2023.12: One paper was accepted to AAAI 2024 (Approximation theory for Offline RL).
2023.11: One paper was accepted to IEEE TPAMI (GNN for Time-series Forecasting).
2023.10: One paper was accepted to Neural Networks (Adaptive SAM).
2023.10: One paper was accepted to IEEE Transactions on Services Computing (hierarchical deep RL for combinatorial optimization).
2023.10: Three papers were accepted to EMNLP 2023 (SAM for data-free quantization, model fusion, Chatgpt for translation).
2023.10: I was invited as an Area Chair for ICPR 2024.
2023.09: Nine papers were accepted to NeurIPS 2023 (efficient training, trustworthy ML, and reinforcement learning).
2023.09: I was invited as an Area Chair for ICLR 2024.
2023.09: One paper was accepted to IEEE TPAMI (distributional robust optimization for continual learning).
2023.09: One paper was accepted to IEICE Trans. Information and Systems (image-to-image translation).
2023.08: One paper was accepted to IEEE TSP (Communication-Efficient Distributed Adam).
2023.08: One paper was accepted to IEEE TNNLS (distributed multi-arm Bandits).
2023.08: One paper was accepted to IEEE TNNLS (global consistency for federated learning).
2023.07: One paper was accepted to ACM MM 2023 (early exit transformer).
2023.07: One paper was accepted to TPAMI (local adaptive federated optimizer).
2023.07: Three papers were accepted to ICCV 2023. (flatness-aware continual learning, SAM for backdoor defense, and federated long-tailed learning)
2023.07: I was invited as a member of Senior Program Committee (SPC) for AAAI 2024.
2023.06: One paper was accepted to TPAMI (safe generative adversarial imitation learning).
2023.06: One paper was accepted to IJCV (sparse training for vanilla GAN).
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)
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.
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.
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.
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.
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.
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.
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, 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. [code]
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.
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.
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang, Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks, ICML, 2020.
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]
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Hanchi Huang, Deheng Ye, Li Shen, Wei Liu, Curriculum-based Asymmetric Multi-task Reinforcement Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 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.
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. [code]
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, A Class of Variable Metric Over-relaxed Hybrid Proximal Extra-gradient Algorithm (In Chinese), PhD Thesis, 2017. [thesis]