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2020 – today
- 2024
[j5]Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen:
Harnessing the Power of Federated Learning in Federated Contextual Bandits. Trans. Mach. Learn. Res. 2024 (2024)- [j4]Kun Yang, Chengshuai Shi, Cong Shen, Jing Yang, Shu-Ping Yeh, Jaroslaw J. Sydir:
Offline Reinforcement Learning for Wireless Network Optimization With Mixture Datasets. IEEE Trans. Wirel. Commun. 23(10): 12703-12716 (2024) - [i15]Chengshuai Shi, Kun Yang, Jing Yang, Cong Shen:
Best Arm Identification for Prompt Learning under a Limited Budget. CoRR abs/2402.09723 (2024) - [i14]Wei Xiong, Chengshuai Shi, Jiaming Shen, Aviv Rosenberg, Zhen Qin, Daniele Calandriello, Misha Khalman, Rishabh Joshi, Bilal Piot, Mohammad Saleh, Chi Jin, Tong Zhang, Tianqi Liu:
Building Math Agents with Multi-Turn Iterative Preference Learning. CoRR abs/2409.02392 (2024) - 2023
- [j3]Chengshuai Shi
, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multiarmed Bandits. IEEE Trans. Signal Process. 71: 4407-4422 (2023) - [c13]Kun Yang, Chengshuai Shi, Cong Shen:
Teaching Reinforcement Learning Agents via Reinforcement Learning. CISS 2023: 1-6 - [c12]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. ICLR 2023 - [c11]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. ICML 2023: 31353-31388 - [c10]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multi-armed Bandits. ISIT 2023: 1454-1459 - [c9]Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos:
On High-dimensional and Low-rank Tensor Bandits. ISIT 2023: 1460-1465 - [i13]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multi-armed Bandits. CoRR abs/2305.02441 (2023) - [i12]Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos:
On High-dimensional and Low-rank Tensor Bandits. CoRR abs/2305.03884 (2023) - [i11]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. CoRR abs/2306.08364 (2023) - [i10]Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen:
Harnessing the Power of Federated Learning in Federated Contextual Bandits. CoRR abs/2312.16341 (2023) - 2022
- [c8]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. ICML 2022: 24496-24523 - [i9]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. CoRR abs/2205.15512 (2022) - [i8]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. CoRR abs/2210.01907 (2022) - 2021
- [j2]Chengshuai Shi, Cong Shen:
On No-Sensing Adversarial Multi-Player Multi-Armed Bandits With Collision Communications. IEEE J. Sel. Areas Inf. Theory 2(2): 515-533 (2021) - [j1]Chengshuai Shi, Cong Shen:
Multi-Player Multi-Armed Bandits With Collision-Dependent Reward Distributions. IEEE Trans. Signal Process. 69: 4385-4402 (2021) - [c7]Chengshuai Shi, Cong Shen:
Federated Multi-Armed Bandits. AAAI 2021: 9603-9611 - [c6]Chengshuai Shi, Cong Shen, Jing Yang:
Federated Multi-armed Bandits with Personalization. AISTATS 2021: 2917-2925 - [c5]Chengshuai Shi, Cong Shen:
An Attackability Perspective on No-Sensing Adversarial Multi-player Multi-armed Bandits. ISIT 2021: 533-538 - [c4]Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen:
(Almost) Free Incentivized Exploration from Decentralized Learning Agents. NeurIPS 2021: 560-571 - [c3]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. NeurIPS 2021: 22392-22404 - [i7]Chengshuai Shi, Cong Shen:
Federated Multi-Armed Bandits. CoRR abs/2101.12204 (2021) - [i6]Chengshuai Shi, Cong Shen, Jing Yang:
Federated Multi-armed Bandits with Personalization. CoRR abs/2102.13101 (2021) - [i5]Chengshuai Shi, Cong Shen:
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions. CoRR abs/2106.13669 (2021) - [i4]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. CoRR abs/2110.14622 (2021) - [i3]Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen:
(Almost) Free Incentivized Exploration from Decentralized Learning Agents. CoRR abs/2110.14628 (2021) - 2020
- [c2]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Decentralized Multi-player Multi-armed Bandits with No Collision Information. AISTATS 2020: 1519-1528 - [i2]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Decentralized Multi-player Multi-armed Bandits with No Collision Information. CoRR abs/2003.00162 (2020) - [i1]Chengshuai Shi, Cong Shen:
On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications. CoRR abs/2011.01090 (2020)
2010 – 2019
- 2019
- [c1]Chengshuai Shi, Lixing Chen, Cong Shen, Linqi Song, Jie Xu:
Privacy-Aware Edge Computing Based on Adaptive DNN Partitioning. GLOBECOM 2019: 1-6
Coauthor Index
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last updated on 2024-10-25 21:18 CEST by the dblp team
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