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ORCIDLi Shen 0008
Person information
- affiliation: JD Explore Academy, Beijing, China
- affiliation: Tencent, Shenzhen, China
- affiliation (PhD 2017): South China University of Technology, Guangzhou, China
- not to be confused with: Li Shen 0005
Other persons with the same name
- Li Shen — disambiguation page
- Li Shen 0001
![[0000-0002-5443-0503]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-5443-0503")
— University of Pennsylvania, Philadelphia, USA (and 3 more) - Li Shen 0002 — Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China
- Li Shen 0003 — Institute for Infocomm Research, Singapore
- Li Shen 0004 — Southwest Jiaotong University, Faculty of Geosciences and Environmental Engineering, Chengdu, China (and 1 more)
- Li Shen 0005 — Alibaba Group, Beijing, China (and 3 more)
- Li Shen 0006 — Osaka University, Graduate School of Information Science and Technology, Japan
- Li Shen 0007 — National University of Defense Technology, School of Computer, Changsha, Hunan, China
- Li Shen 0009 — Beihang University, School of Automation Science and Electrical Engineering, Beijing, China
- Li Shen 0010 — Huazhong University of Science and Technology, School of Optical and Electronic Information, Wuhan, China
- Li Shen 0011 — Beijing Institute of Remote Sensing, China
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2020 – today
- 2024
[j33]Enneng Yang, Xin Xin, Li Shen, Yudong Luo, Guibing Guo:
Generalized Embedding Machines for Recommender Systems. Mach. Intell. Res. 21(3): 571-584 (2024)- [j32]Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, Dacheng Tao:
Local AdaGrad-type algorithm for stochastic convex-concave optimization. Mach. Learn. 113(4): 1819-1838 (2024) - [j31]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 169: 506-519 (2024) - [j30]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 Trans. Pattern Anal. Mach. Intell. 46(4): 2333-2347 (2024) - [j29]Tiehang Duan, Zhenyi Wang, Li Shen, Gianfranco Doretto, Donald A. Adjeroh, Fang Li, Cui Tao:
Retain and Adapt: Online Sequential EEG Classification With Subject Shift. IEEE Trans. Artif. Intell. 5(9): 4479-4492 (2024) - [j28]Derun Song, Enneng Yang, Guibing Guo, Li Shen, Linying Jiang, Xingwei Wang:
Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation System. ACM Trans. Knowl. Discov. Data 18(6): 142:1-142:20 (2024) - [j27]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 Trans. Mob. Comput. 23(10): 9946-9958 (2024) - [j26]Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, Dacheng Tao:
Visual Prompt Based Personalized Federated Learning. Trans. Mach. Learn. Res. 2024 (2024) - [j25]Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao:
Quantum Imitation Learning. IEEE Trans. Neural Networks Learn. Syst. 35(10): 14190-14204 (2024) - [j24]Li Shen, Congliang Chen, Fangyu Zou, Zequn Jie, Ju Sun, Wei Liu:
A Unified Analysis of AdaGrad With Weighted Aggregation and Momentum Acceleration. IEEE Trans. Neural Networks Learn. Syst. 35(10): 14482-14490 (2024) - [j23]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 Trans. Serv. Comput. 17(3): 1001-1015 (2024) - [c85]Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu, Bo Du, Dacheng Tao:
Revisiting Knowledge Distillation for Autoregressive Language Models. ACL (1) 2024: 10900-10913 - [c84]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: 13619-13639 - [c83]Zhiyuan Yu, Li Shen, Liang Ding, Xinmei Tian, Yixin Chen, Dacheng Tao:
Sheared Backpropagation for Fine-Tuning Foundation Models. CVPR 2024: 5883-5892 - [c82]Yingqi Liu, Yifan Shi, Baoyuan Wu, Qinglun Li, Xueqian Wang, Li Shen:
Decentralized Directed Collaboration for Personalized Federated Learning. CVPR 2024: 23168-23178 - [c81]Yongxian Wei, Zixuan Hu, Zhenyi Wang, Li Shen, Chun Yuan, Dacheng Tao:
Free: Faster and Better Data-Free Meta-Learning. CVPR 2024: 23273-23282 - [c80]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: 26233-26243 - [c79]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: 26265-26275 - [c78]Ziming Hong, Li Shen, Tongliang Liu:
Your Transferability Barrier is Fragile: Free-Lunch for Transferring the Non-Transferable Learning. CVPR 2024: 28805-28815 - [c77]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: 2966-2973 - [c76]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 - [c75]Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao:
Learning Multi-Agent Communication from Graph Modeling Perspective. ICLR 2024 - [c74]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 - [c73]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 - [c72]Zhenyi Wang, Yan Li, Li Shen, Heng Huang:
A Unified and General Framework for Continual Learning. ICLR 2024 - [c71]Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, Dacheng Tao:
AdaMerging: Adaptive Model Merging for Multi-Task Learning. ICLR 2024 - [c70]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 - [c69]Shengchao Hu, Ziqing Fan, Li Shen, Ya Zhang, Yanfeng Wang, Dacheng Tao:
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning. ICML 2024 - [c68]Shengchao Hu, Ziqing Fan, Chaoqin Huang, Li Shen, Ya Zhang, Yanfeng Wang, Dacheng Tao:
Q-value Regularized Transformer for Offline Reinforcement Learning. ICML 2024 - [c67]Zixuan Hu, Yongxian Wei, Li Shen, Zhenyi Wang, Lei Li, Chun Yuan, Dacheng Tao:
Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications. ICML 2024 - [c66]Anke Tang, Li Shen, Yong Luo, Nan Yin, Lefei Zhang, Dacheng Tao:
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts. ICML 2024 - [c65]Peng Wang, Li Shen, Zerui Tao, Shuaida He, Dacheng Tao:
Generalization Analysis of Stochastic Weight Averaging with General Sampling. ICML 2024 - [c64]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 - [c63]Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo, Xiaojun Chen, Xingwei Wang, Dacheng Tao:
Representation Surgery for Multi-Task Model Merging. ICML 2024 - [c62]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: 129-138 - [c61]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 Multimedia 2024: 1943-1951 - [c60]Wenbin Wang, Liang Ding, Li Shen, Yong Luo, Han Hu, Dacheng Tao:
WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge. ACM Multimedia 2024: 2282-2291 - [i148]Shuai Wang, Liang Ding, Li Shen, Yong Luo, Bo Du, Dacheng Tao:
OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models. CoRR abs/2401.06628 (2024) - [i147]Wenbin Wang, Liang Ding, Li Shen, Yong Luo, Han Hu, Dacheng Tao:
WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge. CoRR abs/2401.06659 (2024) - [i146]Kaixin Huang, Li Shen, Chen Zhao, Chun Yuan, Dacheng Tao:
Solving Continual Offline Reinforcement Learning with Decision Transformer. CoRR abs/2401.08478 (2024) - [i145]Anke Tang, Li Shen, Yong Luo, Nan Yin, Lefei Zhang, Dacheng Tao:
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts. CoRR abs/2402.00433 (2024) - [i144]Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo, Xiaojun Chen, Xingwei Wang, Dacheng Tao:
Representation Surgery for Multi-Task Model Merging. CoRR abs/2402.02705 (2024) - [i143]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. CoRR abs/2402.07610 (2024) - [i142]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. CoRR abs/2402.11857 (2024) - [i141]Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu, Bo Du, Dacheng Tao:
Revisiting Knowledge Distillation for Autoregressive Language Models. CoRR abs/2402.11890 (2024) - [i140]Zhenyi Wang, Yan Li, Li Shen, Heng Huang:
A Unified and General Framework for Continual Learning. CoRR abs/2403.13249 (2024) - [i139]Changtong Zan, Liang Ding, Li Shen, Yibing Zhen, Weifeng Liu, Dacheng Tao:
Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning. CoRR abs/2403.14399 (2024) - [i138]Yifan Shi, Yuhui Zhang, Ziyue Huang, Xiaofeng Yang, Li Shen, Wei Chen, Xueqian Wang:
A General and Efficient Federated Split Learning with Pre-trained Image Transformers for Heterogeneous Data. CoRR abs/2403.16050 (2024) - [i137]Nan Yin, Mengzhu Wang, Li Shen, Hitesh Laxmichand Patel, Baopu Li, Bin Gu, Huan Xiong:
Continuous Spiking Graph Neural Networks. CoRR abs/2404.01897 (2024) - [i136]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. CoRR abs/2404.15598 (2024) - [i135]Yongxian Wei, Zixuan Hu, Zhenyi Wang, Li Shen, Chun Yuan, Dacheng Tao:
FREE: Faster and Better Data-Free Meta-Learning. CoRR abs/2405.00984 (2024) - [i134]Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao:
Learning Multi-Agent Communication from Graph Modeling Perspective. CoRR abs/2405.08550 (2024) - [i133]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? CoRR abs/2405.12094 (2024) - [i132]Tianqi Liu, Guangcong Wang, Shoukang Hu, Li Shen, Xinyi Ye, Yuhang Zang, Zhiguo Cao, Wei Li, Ziwei Liu:
Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo. CoRR abs/2405.12218 (2024) - [i131]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. CoRR abs/2405.16560 (2024) - [i130]Puning Zhao, Li Shen, Rongfei Fan, Qingming Li, Huiwen Wu, Jiafei Wu, Zhe Liu:
Learning with User-Level Local Differential Privacy. CoRR abs/2405.17079 (2024) - [i129]Shengchao Hu, Ziqing Fan, Chaoqin Huang, Li Shen, Ya Zhang, Yanfeng Wang, Dacheng Tao:
Q-value Regularized Transformer for Offline Reinforcement Learning. CoRR abs/2405.17098 (2024) - [i128]Yingqi Liu, Yifan Shi, Qinglun Li, Baoyuan Wu, Xueqian Wang, Li Shen:
Decentralized Directed Collaboration for Personalized Federated Learning. CoRR abs/2405.17876 (2024) - [i127]Shengchao Hu, Ziqing Fan, Li Shen, Ya Zhang, Yanfeng Wang, Dacheng Tao:
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning. CoRR abs/2405.18080 (2024) - [i126]Longxiang He, Li Shen, Junbo Tan, Xueqian Wang:
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization. CoRR abs/2405.18187 (2024) - [i125]Anke Tang, Li Shen, Yong Luo, Han Hu, Bo Du, Dacheng Tao:
FusionBench: A Comprehensive Benchmark of Deep Model Fusion. CoRR abs/2406.03280 (2024) - [i124]Anke Tang, Li Shen, Yong Luo, Shiwei Liu, Han Hu, Bo Du:
Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion. CoRR abs/2406.09770 (2024) - [i123]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. CoRR abs/2406.17334 (2024) - [i122]Tianjin Huang, Meng Fang, Li Shen, Fan Liu, Yulong Pei, Mykola Pechenizkiy, Shiwei Liu, Tianlong Chen:
(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork. CoRR abs/2407.17412 (2024) - [i121]Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, Dacheng Tao:
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. CoRR abs/2408.07666 (2024) - [i120]Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Li Shen, Puning Zhao, Jie Xu, Han Hu:
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets. CoRR abs/2408.09539 (2024) - [i119]Xingrun Yan, Shiyuan Zuo, Rongfei Fan, Han Hu, Li Shen, Puning Zhao, Yong Luo:
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets. CoRR abs/2408.09762 (2024) - [i118]Anke Tang, Li Shen, Yong Luo, Shuai Xie, Han Hu, Lefei Zhang, Bo Du, Dacheng Tao:
SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models. CoRR abs/2408.10174 (2024) - [i117]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. CoRR abs/2408.10504 (2024) - [i116]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. CoRR abs/2408.15556 (2024) - [i115]Yan Sun, Li Shen, Dacheng Tao:
Convergent Differential Privacy Analysis for General Federated Learning: the f-DP Perspective. CoRR abs/2408.15621 (2024) - [i114]Jifeng Hu, Li Shen, Sili Huang, Zhejian Yang, Hechang Chen, Lichao Sun, Yi Chang, Dacheng Tao:
Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal. CoRR abs/2409.02512 (2024) - [i113]Shuai Wang, Liang Ding, Li Shen, Yong Luo, Zheng He, Wei Yu, Dacheng Tao:
USCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding. CoRR abs/2409.05923 (2024) - [i112]Li Shen, Tianqi Liu, Huiqiang Sun, Xinyi Ye, Baopu Li, Jianming Zhang, Zhiguo Cao:
DreamMover: Leveraging the Prior of Diffusion Models for Image Interpolation with Large Motion. CoRR abs/2409.09605 (2024) - [i111]Yan Sun, Li Shen, Dacheng Tao:
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs. CoRR abs/2409.18915 (2024) - 2023
- [j22]Shiwei Liu, Yuesong Tian, Tianlong Chen, Li Shen:
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance. Int. J. Comput. Vis. 131(10): 2635-2648 (2023) - [j21]Yuesong Tian, Li Shen, Xiang Tian, Zhifeng Li, Yaowu Chen:
Dynamic PDGAN: discriminator-boosted knowledge distillation for StyleGANs. J. Electronic Imaging 33(1) (2023) - [j20]Hanchi Huang, Deheng Ye, Li Shen, Wei Liu:
Curriculum-Based Asymmetric Multi-Task Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(6): 7258-7269 (2023) - [j19]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 Trans. Pattern Anal. Mach. Intell. 45(10): 12236-12249 (2023) - [j18]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 Trans. Pattern Anal. Mach. Intell. 45(12): 14337-14352 (2023) - [j17]Yan Sun, Li Shen, Hao Sun, Liang Ding, Dacheng Tao:
Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 14453-14464 (2023) - [j16]Mengzhu Wang, Shanshan Wang, Wei Wang, Li Shen, Xiang Zhang, Long Lan, Zhigang Luo:
Reducing bi-level feature redundancy for unsupervised domain adaptation. Pattern Recognit. 137: 109319 (2023) - [j15]Han Huang, Li Shen, Chaoyang He, Weisheng Dong, Wei Liu:
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution. IEEE Trans. Circuits Syst. Video Technol. 33(6): 2672-2682 (2023) - [j14]Zixuan Hu, Li Shen, Shenqi Lai, Chun Yuan:
Task-Adaptive Feature Disentanglement and Hallucination for Few-Shot Classification. IEEE Trans. Circuits Syst. Video Technol. 33(8): 3638-3648 (2023) - [j13]Nan Yin, Li Shen, Mengzhu Wang, Xiao Luo, Zhigang Luo, Dacheng Tao:
OMG: Towards Effective Graph Classification Against Label Noise. IEEE Trans. Knowl. Data Eng. 35(12): 12873-12886 (2023) - [j12]Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
FedDAG: Federated DAG Structure Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j11]Tiansheng Huang, Li Shen, Yan Sun, Weiwei Lin, Dacheng Tao:
Fusion of Global and Local Knowledge for Personalized Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j10]Jun Rao, Liang Ding, Shuhan Qi, Meng Fang, Yang Liu, Li Shen, Dacheng Tao:
Dynamic Contrastive Distillation for Image-Text Retrieval. IEEE Trans. Multim. 25: 8383-8395 (2023) - [j9]Congliang Chen, Li Shen, Wei Liu, Zhi-Quan Luo:
Efficient-Adam: Communication-Efficient Distributed Adam. IEEE Trans. Signal Process. 71: 3257-3266 (2023) - [c59]Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao:
Offline Quantum Reinforcement Learning in a Conservative Manner. AAAI 2023: 7148-7156 - [c58]Dui Wang, Li Shen, Yong Luo, Han Hu, Kehua Su, Yonggang Wen, Dacheng Tao:
FedABC: Targeting Fair Competition in Personalized Federated Learning. AAAI 2023: 10095-10103 - [c57]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: 10745-10753 - [c56]Linrui Zhang, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang, Dacheng Tao:
Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks. AAAI 2023: 15313-15321 - [c55]Shenao Zhang, Li Shen, Lei Han, Li Shen:
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning. CoLLAs 2023: 292-317 - [c54]Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao:
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning. CVPR 2023: 7736-7745 - [c53]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CVPR 2023: 16175-16185 - [c52]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: 24521-24531 - [c51]Yifan Shi, Yingqi Liu, Kang Wei, Li Shen, Xueqian Wang, Dacheng Tao:
Make Landscape Flatter in Differentially Private Federated Learning. CVPR 2023: 24552-24562 - [c50]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: 5622-5633 - [c49]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 2023: 11305-11327 - [c48]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 2023: 14685-14691 - [c47]Mingli Zhu, Shaokui Wei, Li Shen, Yanbo Fan, Baoyuan Wu:
Enhancing Fine-Tuning based Backdoor Defense with Sharpness-Aware Minimization. ICCV 2023: 4443-4454 - [c46]Yaopei Zeng, Lei Liu, Li Liu, Li Shen, Shaoguo Liu, Baoyuan Wu:
Global Balanced Experts for Federated Long-Tailed Learning. ICCV 2023: 4792-4802 - [c45]Enneng Yang, Li Shen, Zhenyi Wang, Shiwei Liu, Guibing Guo, Xingwei Wang:
Data Augmented Flatness-aware Gradient Projection for Continual Learning. ICCV 2023: 5607-5616 - [c44]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 - [c43]Yan Sun, Li Shen, Tiansheng Huang, Liang Ding, Dacheng Tao:
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy. ICLR 2023 - [c42]Runzhong Wang, Li Shen, Yiting Chen, Xiaokang Yang, Dacheng Tao, Junchi Yan:
Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case. ICLR 2023 - [c41]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: 13610-13627 - [c40]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: 14023-14038 - [c39]Yifan Shi, Li Shen, Kang Wei, Yan Sun, Bo Yuan, Xueqian Wang, Dacheng Tao:
Improving the Model Consistency of Decentralized Federated Learning. ICML 2023: 31269-31291 - [c38]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: 32991-33013 - [c37]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: 40040-40053 - [c36]Zhihao Cheng, Li Shen, Dacheng Tao:
Off-policy Imitation Learning from Visual Inputs. ICRA 2023: 2937-2943 - [c35]Guanyu Xu, Jiawei Hao, Li Shen, Han Hu, Yong Luo, Hui Lin, Jialie Shen:
LGViT: Dynamic Early Exiting for Accelerating Vision Transformer. ACM Multimedia 2023: 9103-9114 - [c34]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 - [c33]Xuming An, Li Shen, Han Hu, Yong Luo:
Federated Learning with Manifold Regularization and Normalized Update Reaggregation. NeurIPS 2023 - [c32]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 - [c31]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 - [c30]Rui Min, Zeyu Qin, Li Shen, Minhao Cheng:
Towards Stable Backdoor Purification through Feature Shift Tuning. NeurIPS 2023 - [c29]Yan Sun, Li Shen, Dacheng Tao:
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization. NeurIPS 2023 - [c28]Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, David S. Doermann, Mingchen Gao:
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training. NeurIPS 2023 - [c27]Enneng Yang, Li Shen, Zhenyi Wang, Tongliang Liu, Guibing Guo:
An Efficient Dataset Condensation Plugin and Its Application to Continual Learning. NeurIPS 2023 - [c26]Miaoxi Zhu, Li Shen, Bo Du, Dacheng Tao:
Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm. NeurIPS 2023 - [c25]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/PKDD (1) 2023: 113-130 - [i110]Qin Zhang, Linrui Zhang, Haoran Xu, Li Shen, Bowen Wang, Yongzhe Chang, Xueqian Wang, Bo Yuan, Dacheng Tao:
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning. CoRR abs/2301.12203 (2023) - [i109]Yifan Shi, Li Shen, Kang Wei, Yan Sun, Bo Yuan, Xueqian Wang, Dacheng Tao:
Improving the Model Consistency of Decentralized Federated Learning. CoRR abs/2302.04083 (2023) - [i108]Yixing Liu, Yan Sun, Zhengtao Ding, Li Shen, Bo Liu, Dacheng Tao:
Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach. CoRR abs/2302.05726 (2023) - [i107]Dui Wang, Li Shen, Yong Luo, Han Hu, Kehua Su, Yonggang Wen, Dacheng Tao:
FedABC: Targeting Fair Competition in Personalized Federated Learning. CoRR abs/2302.07450 (2023) - [i106]Qihuang Zhong, Liang Ding, Keqin Peng, Juhua Liu, Bo Du, Li Shen, Yibing Zhan, Dacheng Tao:
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE. CoRR abs/2302.09268 (2023) - [i105]Yan Sun, Li Shen, Tiansheng Huang, Liang Ding, Dacheng Tao:
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy. CoRR abs/2302.10429 (2023) - [i104]Tiansheng Huang, Li Shen, Yan Sun, Weiwei Lin, Dacheng Tao:
Fusion of Global and Local Knowledge for Personalized Federated Learning. CoRR abs/2302.11051 (2023) - [i103]Guanghao Li, Li Shen, Yan Sun, Yue Hu, Han Hu, Dacheng Tao:
Subspace based Federated Unlearning. CoRR abs/2302.12448 (2023) - [i102]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, Dacheng Tao:
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System. CoRR abs/2303.00501 (2023) - [i101]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. CoRR abs/2303.00565 (2023) - [i100]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. CoRR abs/2303.03625 (2023) - [i99]Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao:
Graph Decision Transformer. CoRR abs/2303.03747 (2023) - [i98]Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, Dacheng Tao:
Visual Prompt Based Personalized Federated Learning. CoRR abs/2303.08678 (2023) - [i97]Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao:
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning. CoRR abs/2303.11183 (2023) - [i96]Yifan Shi, Yingqi Liu, Kang Wei, Li Shen, Xueqian Wang, Dacheng Tao:
Make Landscape Flatter in Differentially Private Federated Learning. CoRR abs/2303.11242 (2023) - [i95]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CoRR abs/2303.13087 (2023) - [i94]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. CoRR abs/2303.13780 (2023) - [i93]Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao:
Quantum Imitation Learning. CoRR abs/2304.02480 (2023) - [i92]Li Shen, Yan Sun, Zhiyuan Yu, Liang Ding, Xinmei Tian, Dacheng Tao:
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review. CoRR abs/2304.03589 (2023) - [i91]Mingli Zhu, Shaokui Wei, Li Shen, Yanbo Fan, Baoyuan Wu:
Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization. CoRR abs/2304.11823 (2023) - [i90]Yifan Shi, Kang Wei, Li Shen, Yingqi Liu, Xueqian Wang, Bo Yuan, Dacheng Tao:
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy. CoRR abs/2305.00873 (2023) - [i89]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. CoRR abs/2305.01387 (2023) - [i88]Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao:
Prompt-Tuning Decision Transformer with Preference Ranking. CoRR abs/2305.09648 (2023) - [i87]Yan Sun, Li Shen, Shixiang Chen, Liang Ding, Dacheng Tao:
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. CoRR abs/2305.11584 (2023) - [i86]Yifan Shi, Yingqi Liu, Yan Sun, Zihao Lin, Li Shen, Xueqian Wang, Dacheng Tao:
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training. CoRR abs/2305.15157 (2023) - [i85]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. CoRR abs/2305.16379 (2023) - [i84]Zixuan Hu, Li Shen, Zhenyi Wang, Baoyuan Wu, Chun Yuan, Dacheng Tao:
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning. CoRR abs/2305.18413 (2023) - [i83]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? CoRR abs/2305.19412 (2023) - [i82]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. CoRR abs/2305.19454 (2023) - [i81]Jifeng Hu, Yanchao Sun, Sili Huang, Siyuan Guo, Hechang Chen, Li Shen, Lichao Sun, Yi Chang, Dacheng Tao:
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning. CoRR abs/2306.04875 (2023) - [i80]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. CoRR abs/2306.04979 (2023) - [i79]Yan Sun, Li Shen, Dacheng Tao:
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization. CoRR abs/2306.05706 (2023) - [i78]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. CoRR abs/2306.14275 (2023) - [i77]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. CoRR abs/2306.17504 (2023) - [i76]Zihao Zhu, Mingda Zhang, Shaokui Wei, Li Shen, Yanbo Fan, Baoyuan Wu:
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy. CoRR abs/2307.07328 (2023) - [i75]Zhenyi Wang, Enneng Yang, Li Shen, Heng Huang:
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. CoRR abs/2307.09218 (2023) - [i74]Guanyu Xu, Jiawei Hao, Li Shen, Han Hu, Yong Luo, Hui Lin, Jialie Shen:
LGViT: Dynamic Early Exiting for Accelerating Vision Transformer. CoRR abs/2308.00255 (2023) - [i73]Yan Sun, Li Shen, Hao Sun, Liang Ding, Dacheng Tao:
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup. CoRR abs/2308.00522 (2023) - [i72]Qinglun Li, Li Shen, Guanghao Li, Quanjun Yin, Dacheng Tao:
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning. CoRR abs/2308.08290 (2023) - [i71]Xiaoge Deng, Li Shen, Shengwei Li, Tao Sun, Dongsheng Li, Dacheng Tao:
Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent. CoRR abs/2308.09430 (2023) - [i70]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. CoRR abs/2308.12680 (2023) - [i69]Fei Wang, Liang Ding, Jun Rao, Ye Liu, Li Shen, Changxing Ding:
Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining? CoRR abs/2308.12898 (2023) - [i68]Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao:
MerA: Merging Pretrained Adapters For Few-Shot Learning. CoRR abs/2308.15982 (2023) - [i67]Enneng Yang, Zhenyi Wang, Li Shen, Nan Yin, Tongliang Liu, Guibing Guo, Xingwei Wang, Dacheng Tao:
Continual Learning From a Stream of APIs. CoRR abs/2309.00023 (2023) - [i66]Hao Sun, Li Shen, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao:
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data. CoRR abs/2309.09719 (2023) - [i65]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? CoRR abs/2309.11166 (2023) - [i64]Weishi Li, Yong Peng, Miao Zhang, Liang Ding, Han Hu, Li Shen:
Deep Model Fusion: A Survey. CoRR abs/2309.15698 (2023) - [i63]Changtong Zan, Liang Ding, Li Shen, Yibin Lei, Yibing Zhan, Weifeng Liu, Dacheng Tao:
Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation. CoRR abs/2309.16599 (2023) - [i62]Rui Min, Zeyu Qin, Li Shen, Minhao Cheng:
Towards Stable Backdoor Purification through Feature Shift Tuning. CoRR abs/2310.01875 (2023) - [i61]Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, Dacheng Tao:
AdaMerging: Adaptive Model Merging for Multi-Task Learning. CoRR abs/2310.02575 (2023) - [i60]Zihao Lin, Yan Sun, Yifan Shi, Xueqian Wang, Lifu Huang, Li Shen, Dacheng Tao:
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models. CoRR abs/2310.03123 (2023) - [i59]Yan Sun, Li Shen, Dacheng Tao:
Which mode is better for federated learning? Centralized or Decentralized. CoRR abs/2310.03461 (2023) - [i58]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. CoRR abs/2310.04742 (2023) - [i57]Qinglun Li, Miao Zhang, Nan Yin, Quanjun Yin, Li Shen:
Asymmetrically Decentralized Federated Learning. CoRR abs/2310.05093 (2023) - [i56]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. CoRR abs/2310.07418 (2023) - [i55]Hongling Zheng, Li Shen, Anke Tang, Yong Luo, Han Hu, Bo Du, Dacheng Tao:
Learn From Model Beyond Fine-Tuning: A Survey. CoRR abs/2310.08184 (2023) - [i54]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. CoRR abs/2310.09762 (2023) - [i53]Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao:
Merging Experts into One: Improving Computational Efficiency of Mixture of Experts. CoRR abs/2310.09832 (2023) - [i52]Miaoxi Zhu, Qihuang Zhong, Li Shen, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao:
Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models. CoRR abs/2310.13315 (2023) - [i51]Tao Sun, Congliang Chen, Peng Qiao, Li Shen, Xinwang Liu, Dongsheng Li:
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz. CoRR abs/2310.14616 (2023) - [i50]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. CoRR abs/2310.16391 (2023) - [i49]Zhuo Huang, Li Shen, Jun Yu, Bo Han, Tongliang Liu:
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning. CoRR abs/2310.16412 (2023) - [i48]Miaoxi Zhu, Li Shen, Bo Du, Dacheng Tao:
Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm. CoRR abs/2310.20369 (2023) - [i47]Xuming An, Li Shen, Han Hu, Yong Luo:
Federated Learning with Manifold Regularization and Normalized Update Reaggregation. CoRR abs/2311.05924 (2023) - [i46]Zixuan Hu, Li Shen, Zhenyi Wang, Yongxian Wei, Baoyuan Wu, Chun Yuan, Dacheng Tao:
Task-Distributionally Robust Data-Free Meta-Learning. CoRR abs/2311.14756 (2023) - [i45]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. CoRR abs/2311.16714 (2023) - [i44]Anke Tang, Li Shen, Yong Luo, Liang Ding, Han Hu, Bo Du, Dacheng Tao:
Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion. CoRR abs/2312.06173 (2023) - 2022
- [j8]Tiansheng Huang, Weiwei Lin, Li Shen, Keqin Li, Albert Y. Zomaya:
Stochastic Client Selection for Federated Learning With Volatile Clients. IEEE Internet Things J. 9(20): 20055-20070 (2022) - [j7]Chuang Zhang, Li Shen, Jian Yang, Chen Gong:
Towards harnessing feature embedding for robust learning with noisy labels. Mach. Learn. 111(9): 3181-3201 (2022) - [j6]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 151: 238-249 (2022) - [j5]Yuesong Tian, Li Shen, Li Shen, Guinan Su, Zhifeng Li, Wei Liu:
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 6752-6766 (2022) - [c24]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: 5029-5034 - [c23]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: 7972-7982 - [c22]Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, Ling-Yu Duan:
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning. CVPR 2022: 10164-10173 - [c21]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 (20) 2022: 221-238 - [c20]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. EMNLP (Findings) 2022: 4064-4085 - [c19]Shaopeng Fu, Fengxiang He, Yang Liu, Li Shen, Dacheng Tao:
Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning. ICLR 2022 - [c18]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 - [c17]Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, Dacheng Tao:
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. ICML 2022: 4587-4604 - [c16]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: 13857-13869 - [c15]Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao:
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution. ICML 2022: 22985-22998 - [c14]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. ICML 2022: 25595-25610 - [c13]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Du Bo, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. IJCAI 2022: 3688-3694 - [c12]Linrui Zhang, Li Shen, Long Yang, Shixiang Chen, Xueqian Wang, Bo Yuan, Dacheng Tao:
Penalized Proximal Policy Optimization for Safe Reinforcement Learning. IJCAI 2022: 3744-3750 - [c11]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: 9027-9033 - [c10]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 Multimedia 2022: 3470-3479 - [c9]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. NeurIPS 2022 - [c8]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 - [c7]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 - [c6]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: 2045-2055 - [i43]Tiansheng Huang, Shiwei Liu, Li Shen, Fengxiang He, Weiwei Lin, Dacheng Tao:
Achieving Personalized Federated Learning with Sparse Local Models. CoRR abs/2201.11380 (2022) - [i42]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. CoRR abs/2202.02643 (2022) - [i41]Shiwei Liu, Yuesong Tian, Tianlong Chen, Li Shen:
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance. CoRR abs/2203.02770 (2022) - [i40]Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, Ling-Yu Duan:
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning. CoRR abs/2203.09249 (2022) - [i39]Shaopeng Fu, Fengxiang He, Yang Liu, Li Shen, Dacheng Tao:
Robust Unlearnable Examples: Protecting Data Against Adversarial Learning. CoRR abs/2203.14533 (2022) - [i38]Changtong Zan, Liang Ding, Li Shen, Yu Cao, Weifeng Liu, Dacheng Tao:
Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation. CoRR abs/2204.07834 (2022) - [i37]Linrui Zhang, Li Shen, Long Yang, Shixiang Chen, Bo Yuan, Xueqian Wang, Dacheng Tao:
Penalized Proximal Policy Optimization for Safe Reinforcement Learning. CoRR abs/2205.11814 (2022) - [i36]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. CoRR abs/2205.13869 (2022) - [i35]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. CoRR abs/2205.14826 (2022) - [i34]Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, Dacheng Tao:
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. CoRR abs/2206.00187 (2022) - [i33]Linrui Zhang, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang:
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving. CoRR abs/2206.08528 (2022) - [i32]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. CoRR abs/2206.08675 (2022) - [i31]Chuang Zhang, Li Shen, Jian Yang, Chen Gong:
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels. CoRR abs/2206.13025 (2022) - [i30]Jun Rao, Liang Ding, Shuhan Qi, Meng Fang, Yang Liu, Li Shen, Dacheng Tao:
Dynamic Contrastive Distillation for Image-Text Retrieval. CoRR abs/2207.01426 (2022) - [i29]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. CoRR abs/2207.03162 (2022) - [i28]Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao:
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution. CoRR abs/2207.07256 (2022) - [i27]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. CoRR abs/2209.01501 (2022) - [i26]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. CoRR abs/2209.03316 (2022) - [i25]Chaojian Yu, Dawei Zhou, Li Shen, Jun Yu, Bo Han, Mingming Gong, Nannan Wang, Tongliang Liu:
Strength-Adaptive Adversarial Training. CoRR abs/2210.01288 (2022) - [i24]Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Xiaoshuai Sun, Rongrong Ji, Dacheng Tao:
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach. CoRR abs/2210.05177 (2022) - [i23]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. CoRR abs/2210.05497 (2022) - [i22]Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, Baoyuan Wu:
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation. CoRR abs/2210.05968 (2022) - [i21]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. CoRR abs/2211.15055 (2022) - [i20]Qihuang Zhong, Liang Ding, Yibing Zhan, Yu Qiao, Yonggang Wen, Li Shen, Juhua Liu, Baosheng Yu, Bo Du, Yixin Chen, Xinbo Gao, Chunyan Miao, Xiaoou Tang, Dacheng Tao:
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE. CoRR abs/2212.01853 (2022) - [i19]Linrui Zhang, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang, Dacheng Tao:
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks. CoRR abs/2212.05727 (2022) - [i18]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. CoRR abs/2212.06998 (2022) - [i17]Shengchao Hu, Li Shen, Ya Zhang, Yixin Chen, Dacheng Tao:
On Transforming Reinforcement Learning by Transformer: The Development Trajectory. CoRR abs/2212.14164 (2022) - 2021
- [j4]Ye Tian, Meiling Chen, Li Shen, Bo Jiang, Zhifeng Li:
Knowledge Distillation With Multi-Objective Divergence Learning. IEEE Signal Process. Lett. 28: 962-966 (2021) - [j3]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 Trans. Image Process. 30: 6107-6116 (2021) - [j2]Congliang Chen, Li Shen, Hao-Zhi Huang, Wei Liu:
Quantized Adam with Error Feedback. ACM Trans. Intell. Syst. Technol. 12(5): 56:1-56:26 (2021) - [c5]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: 9908-9922 - [i16]Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, Dacheng Tao:
Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems. CoRR abs/2106.10022 (2021) - [i15]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. CoRR abs/2106.10404 (2021) - [i14]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. CoRR abs/2108.05650 (2021) - [i13]Xinyue Wei, Haozhi Huang, Yujin Shi, Hongliang Yuan, Li Shen, Jue Wang:
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution. CoRR abs/2108.06915 (2021) - [i12]Shenao Zhang, Li Shen, Lei Han, Li Shen:
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning. CoRR abs/2108.12988 (2021) - [i11]Zhihao Cheng, Li Shen, Dacheng Tao:
Off-policy Imitation Learning from Visual Inputs. CoRR abs/2111.04345 (2021) - [i10]Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard D. Bondell:
Federated Causal Discovery. CoRR abs/2112.03555 (2021) - [i9]Shiye Lei, Zhuozhuo Tu, Leszek Rutkowski, Feng Zhou, Li Shen, Fengxiang He, Dacheng Tao:
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer. CoRR abs/2112.06281 (2021) - [i8]Yuesong Tian, Li Shen, Dacheng Tao, Zhifeng Li, Wei Liu:
DGL-GAN: Discriminator Guided Learning for GAN Compression. CoRR abs/2112.06502 (2021) - 2020
- [j1]Baoyuan Wu, Li Shen, Tong Zhang, Bernard Ghanem:
MAP Inference Via ℓ 2-Sphere Linear Program Reformulation. Int. J. Comput. Vis. 128(7): 1913-1936 (2020) - [c4]Ganzhao Yuan, Li Shen, Wei-Shi Zheng:
A Block Decomposition Algorithm for Sparse Optimization. KDD 2020: 275-285 - [i7]Enneng Yang, Xin Xin, Li Shen, Guibing Guo:
Generalized Embedding Machines for Recommender Systems. CoRR abs/2002.06561 (2020) - [i6]Yuesong Tian, Li Shen, Li Shen, Guinan Su, Zhifeng Li, Wei Liu:
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks. CoRR abs/2006.09134 (2020) - [i5]Meng Cao, Haozhi Huang, Hao Wang, Xuan Wang, Li Shen, Sheng Wang, Linchao Bao, Zhifeng Li, Jiebo Luo:
Task-agnostic Temporally Consistent Facial Video Editing. CoRR abs/2007.01466 (2020)
2010 – 2019
- 2019
- [c3]Ganzhao Yuan, Li Shen, Wei-Shi Zheng:
A Decomposition Algorithm for the Sparse Generalized Eigenvalue Problem. CVPR 2019: 6113-6122 - [c2]Guibing Guo, Enneng Yang, Li Shen, Xiaochun Yang, Xiaodong He:
Discrete Trust-aware Matrix Factorization for Fast Recommendation. IJCAI 2019: 1380-1386 - [i4]Baoyuan Wu, Li Shen, Bernard Ghanem, Tong Zhang:
MAP Inference via L2-Sphere Linear Program Reformulation. CoRR abs/1905.03433 (2019) - [i3]Ganzhao Yuan, Li Shen, Wei-Shi Zheng:
A Block Decomposition Algorithm for Sparse Optimization. CoRR abs/1905.11031 (2019) - 2018
- [i2]Ganzhao Yuan, Li Shen, Wei-Shi Zheng:
A Decomposition Algorithm for Sparse Generalized Eigenvalue Problem. CoRR abs/1802.09303 (2018) - [i1]Ganzhao Yuan, Wei-Shi Zheng, Li Shen, Bernard Ghanem:
A Generalized Matrix Splitting Algorithm. CoRR abs/1806.03165 (2018) - 2017
- [c1]Li Shen, Wei Liu, Junzhou Huang, Yu-Gang Jiang, Shiqian Ma:
Adaptive Proximal Average Approximation for Composite Convex Minimization. AAAI 2017: 2513-2519
Coauthor Index
[j32] [j31] [j26] [j25] [c85] [c84] [c83] [c81] [c77] [c75] [c74] [c73] [c71] [c69] [c68] [c67] [c66] [c65] [c64] [c63] [c60] [i148] [i147] [i146] [i145] [i144] [i142] [i141] [i139] [i136] [i135] [i134] [i131] [i129] [i127] [i125] [i121] [i118] [i117] [i116] [i115] [i114] [i113] [i111] [j19] [j17] [j13] [j11] [j10] [c59] [c58] [c56] [c54] [c51] [c50] [c49] [c48] [c43] [c42] [c41] [c39] [c38] [c36] [c31] [c29] [c26] [i110] [i109] [i108] [i107] [i106] [i105] [i104] [i103] [i102] [i101] [i99] [i98] [i97] [i96] [i94] [i93] [i92] [i90] [i88] [i87] [i86] [i85] [i84] [i81] [i79] [i77] [i73] [i72] [i71] [i68] [i67] [i66] [i65] [i63] [i61] [i60] [i59] [i58] [i56] [i55] [i54] [i53] [i52] [i48] [i46] [i44] [c24] [c22] [c20] [c19] [c17] [c12] [c11] [c8] [i43] [i40] [i39] [i38] [i37] [i34] [i30] [i26] [i24] [i23] [i20] [i19] [i18] [i17] [i16] [i11] [i9] [i8]
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