Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao, Improving Task-free Continual Learning by Distributionally Robust Memory Evolution, ICML, 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. Įrdun 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. 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. 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. 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. Yan Sun, Li Shen, Tiansheng Huang, Liang Ding, Dacheng Tao, FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy, ICLR, 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. 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. Yifan Shi, Li Shen, Kang Wei, Yan Sun, Bo Yuan, Xueqian Wang, Dacheng Tao, Improving the Model Consistency of Decentralized Federated Learning, ICML, 2023. 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. 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. (flatness-aware continual learning SAM for backdoor defense and federated long-tail ed learning)Ģ023.07 : I was invited as a member of Senior Program Committee (SPC) for AAAI 2024.Ģ023.0 6: One paper was accepted to TPAMI (safe generative adversarial imitation learning).Ģ023.0 6 : One paper was accepted to IJCV ( sparse training for vanilla GAN ). Information and Systems ( image-to-image translation ).Ģ023.08: One paper was accepted to IEEE TSP (Communication-Efficient Distributed Adam).Ģ023.08: One paper was accepted to IEEE TNNLS (distributed multi-arm Bandits).Ģ023.08 : One paper was accepted to IEEE TNNLS (global consistency for federated learning).Ģ023.07 : One paper was accepted to ACM MM 2023 ( early exit transformer ).Ģ023.07: One paper was accepted to TPAMI ( local adaptive federated optimizer ).Ģ023.07: Three papers were accepted to ICCV 2023. I am open to collaborating with remote students and visitors (with or without research experience) who are interested in optimization, federated learning, efficient training/ tuning of foundation models and large language models, human-in-the-loop learning (rlhf, alignment, rank preference, etc.), reinforcement learning, etc.Ģ023.0 9 : One paper was accepted to TPAMI ( distributional robust optimization for continual learning ).Ģ023.0 9 : One paper was accepted to IEICE Trans. The internship position requires full-time work for at least 6 months. Recruiting several self-motivated research interns to work on zero/first/second-order distributed/federated learning, reinforcement learning, foundation models, and large language models (efficient-training, fine-tuning, quantization, etc.).

Recruiting several full-time research scientists to work on RL/ML for combinatorial optimization (VRP, Bin Packing, ect.), MARL, and embodied AI. His research interests include theory and algorithms for nonsmooth convex and nonconvex optimization, and their applications in operation research, game theory, statistical machine learning, deep learning, and reinforcement learning. from the School of Mathematics, South China University of Technology. He received his bachelor's degree and Ph.D.

Previously, he was a senior researcher at Tencent AI Lab. Li Shen is currently a research scientist at JD Explore Academy, Beijing, China.

Jing dong google scholar