5 citations · 5 across the 5 of their papers we have counts for
5 papers
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
Nikki Lijing Kuang, Ming Yin, Mengdi Wang +2
Recent studies in reinforcement learning (RL) have made significant progress by leveraging function approximation to alleviate the sample complexity hurdle for better performance.…
Communication-Efficient Federated Non-Linear Bandit Optimization
Chuanhao Li, Chong Liu, Yu-Xiang Wang
Federated optimization studies the problem of collaborative function optimization among multiple clients (e.g. mobile devices or organizations) under the coordination of a central…
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen +3
This paper focuses on supervised and unsupervised online label shift, where the class marginals varies but the class-conditionals remain invariant. In the unsupervi…
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs
Dan Qiao, Ming Yin, Yu-Xiang Wang
In many real-life reinforcement learning (RL) problems, deploying new policies is costly. In those scenarios, algorithms must solve exploration (which requires adaptivity) while sw…
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise
Jianyu Xu, Yu-Xiang Wang
In feature-based dynamic pricing, a seller sets appropriate prices for a sequence of products (described by feature vectors) on the fly by learning from the binary outcomes of prev…