5 papers
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang +5
For question-answering (QA) tasks, in-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the…
Provable Zero-Shot Generalization in Offline Reinforcement Learning
Zhiyong Wang, Chen Yang, John C. S. Lui +1
In this work, we study offline reinforcement learning (RL) with zero-shot generalization property (ZSG), where the agent has access to an offline dataset including experiences from…
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds
Zhiyong Wang, Dongruo Zhou, John C. S. Lui +1
Learning a transition model via Maximum Likelihood Estimation (MLE) followed by planning inside the learned model is perhaps the most standard and simplest Model-based Reinforcemen…
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
Zhiyong Wang, Jize Xie, Yi Chen +2
We investigate the non-stationary stochastic linear bandit problem where the reward distribution evolves each round. Existing algorithms characterize the non-stationarity by the to…
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
Hantao Yang, Xutong Liu, Zhiyong Wang +4
We study the problem of federated contextual combinatorial cascading bandits, where agents collaborate under the coordination of a central server to provide tailore…