Publication

Journal Papers

1. C. Shi, W. Xiong, C. Shen, and J. Yang, “Reward Teaching for Federated Multi-Armed Bandits”, IEEE Trans. on Signal Processing, vol.71, pp.4407-4422, Nov. 2023

2. C. Shi and C. Shen, “Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions”, IEEE Trans. Signal Processing, vol. 69, pp. 4385–4402, July 2021

3. C. Shi and C. Shen, “On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications”, IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 2, pp. 515-533, June 2021

Conference Papers

1. C. Shi, R. Zhou, K. Yang, and C. Shen, “Harnessing the Power of Federated Learning in Federated Contextual Bandits”, The Multi-Agent Security Workshop at The 37th Conference on Neural Information Processing Systems (MASEC@NeurIPS 2023), Dec. 2023

2. C. Shi, W. Xiong, C. Shen, and J. Yang, “Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources”, The 40th International Conference on Machine Learning (ICML 2023), June 2023

3. C. Shi, C. Shen, and N. D. Sidiropoulos, “On High-Dimensional and Low-Rank Tensor Bandits”, 2023 IEEE International Symposium on Information Theory (ISIT 2023), June 2023

4. C. Shi, W. Xiong, C. Shen, and J. Yang, “Reward Teaching for Federated Multi-Armed Bandits”, 2023 IEEE International Symposium on Information Theory (ISIT 2023), June 2023

5. K. Yang, C. Shi, and C. Shen, “Teaching Reinforcement Learning Agents via Reinforcement Learning”, 57th Annual Conference on Information Sciences and Systems (CISS 2023), March 2023 (Invited Paper)

6. W. Xiong, H. Zhong, C. Shi, C. Shen, L. Wang, and T. Zhang, “Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game”, The Eleventh International Conference on Learning Representations (ICLR 2023), May 2023

7. W. Xiong, H. Zhong, C. Shi, C. Shen, and T. Zhang, “A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games”, The 39th International Conference on Machine Learning (ICML 2022), July 2022

   1. Also appears at the 10th International Conference on Learning Representations (ICLR 2022) Workshop on Gamification and Multiagent Solutions

8. C. Shi, W. Xiong, C. Shen, and J. Yang, “Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization”, The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Dec. 2021

9. C. Shi, H. Xu, W. Xiong, and C. Shen, “(Almost) Free Incentivized Exploration from Decentralized Learning Agents”, The 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Dec. 2021

10. C. Shi and C. Shen, “An Attackability Perspective on No-Sensing Adversarial Multi-player Multi-armed Bandits”, 2021 IEEE International Symposium on Information Theory (ISIT 2021), July 2021

11. C. Shi, C. Shen, and J. Yang, “Federated Multi-armed Bandits with Personalization”, The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), Apr. 2021 (Oral Presentation, 48/1527 = 3%)

12. C. Shi and C. Shen, “Federated Multi-Armed Bandits”, The 35th AAAI Conference on Artificial Intelligence (AAAI 2021), Feb. 2021

13. C. Shi, W. Xiong, C. Shen, and J. Yang, “Decentralized Multi-player Multi-armed Bandits with No Collision Information”, The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Aug. 2020

14. C. Shi, L. Chen, C. Shen, L. Song, and J. Xu, “Privacy-Aware Edge Computing Based on Adaptive DNN Partitioning”, IEEE Globecom, Dec. 2019