15 citations · 61 across the 28 of their papers we have counts for
9 papers · 1 filter
Cost-Aware Optimal Pairwise Pure Exploration
Di Wu, Chengshuai Shi, Ruida Zhou +1
Pure exploration is one of the fundamental problems in multi-armed bandits (MAB). However, existing works mostly focus on specific pure exploration tasks, without a holistic view o…
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models
Chengshuai Shi, Kun Yang, Jing Yang +1
The in-context learning (ICL) capability of pre-trained models based on the transformer architecture has received growing interest in recent years. While theoretical understanding…
Efficient Prompt Optimization Through the Lens of Best Arm Identification
Chengshuai Shi, Kun Yang, Zihan Chen +3
The remarkable instruction-following capability of large language models (LLMs) has sparked a growing interest in automatically finding good prompts, i.e., prompt optimization. Mos…
Harnessing the Power of Federated Learning in Federated Contextual Bandits
Chengshuai Shi, Ruida Zhou, Kun Yang +1
Federated learning (FL) has demonstrated great potential in revolutionizing distributed machine learning, and tremendous efforts have been made to extend it beyond the original foc…
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
Chengshuai Shi, Wei Xiong, Cong Shen +1
Existing theoretical studies on offline reinforcement learning (RL) mostly consider a dataset sampled directly from the target task. In practice, however, data often come from seve…
On High-dimensional and Low-rank Tensor Bandits
Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos
Most existing studies on linear bandits focus on the one-dimensional characterization of the overall system. While being representative, this formulation may fail to model applicat…
Reward Teaching for Federated Multi-armed Bandits
Chengshuai Shi, Wei Xiong, Cong Shen +1
Most of the existing federated multi-armed bandits (FMAB) designs are based on the presumption that clients will implement the specified design to collaborate with the server. In r…
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
Chengshuai Shi, Wei Xiong, Cong Shen +1
Despite the significant interests and many progresses in decentralized multi-player multi-armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized…
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
Chengshuai Shi, Haifeng Xu, Wei Xiong +1
Incentivized exploration in multi-armed bandits (MAB) has witnessed increasing interests and many progresses in recent years, where a principal offers bonuses to agents to do explo…