4 citations · 10 across the 13 of their papers we have counts for
7 papers · 1 filter
Distributed Thompson sampling under constrained communication
Saba Zerefa, Zhaolin Ren, Haitong Ma +1
In Bayesian optimization, a black-box function is maximized via the use of a surrogate model. We apply distributed Thompson sampling, using a Gaussian process as a surrogate model,…
Enhancing Preference-based Linear Bandits via Human Response Time
Shen Li, Yuyang Zhang, Zhaolin Ren +3
Interactive preference learning systems infer human preferences by presenting queries as pairs of options and collecting binary choices. Although binary choices are simple and wide…
Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint
Haitong Ma, Zhaolin Ren, Bo Dai +1
We study sim-to-real skill transfer and discovery in the context of robotics control using representation learning. We draw inspiration from spectral decomposition of Markov decisi…
TS-RSR: A provably efficient approach for batch Bayesian Optimization
Zhaolin Ren, Na Li
This paper presents a new approach for batch Bayesian Optimization (BO) called Thompson Sampling-Regret to Sigma Ratio directed sampling (TS-RSR), where we sample a new batch of ac…
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding
Zhaolin Ren, Tongzheng Ren, Haitong Ma +2
This paper proposes an approach, Spectral Dynamics Embedding Control (SDEC), to optimal control for nonlinear stochastic systems. This method reveals an infinite-dimensional featur…
FedDAR: Federated Domain-Aware Representation Learning
Aoxiao Zhong, Hao He, Zhaolin Ren +2
Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient dat…
Gradient play in stochastic games: stationary points, convergence, and sample complexity
Runyu Zhang, Zhaolin Ren, Na Li
We study the performance of the gradient play algorithm for stochastic games (SGs), where each agent tries to maximize its own total discounted reward by making decisions independe…