1 citations · 1 across the 7 of their papers we have counts for
8 papers
Quantum Spectral Estimation with Multiple Initial States: Improved Error and Cost Scaling
Lingwei Zhang, Zhiyan Ding
We study quantum spectrum and eigenstate property estimation using multiple initial states and generalized Hadamard tests. Building on the QFAMES (quantum filtering and analysis of…
Breaking the Total Variance Barrier: Sharp Sample Complexity for Linear Heteroscedastic Bandits with Fixed Action Set
Heyang Zhao, Tianyuan Jin, Weixin Wang +3
Recent years have witnessed increasing interests in tackling heteroscedastic noise in bandits and reinforcement learning. In these works, the cumulative variance of the noise $Λ= \…
Inference-Time Alignment of Diffusion Models via Trust-Region Iterative Twisted Sequential Monte Carlo
Weixin Wang, Yu Yang, Wei Deng +1
We study inference-time alignment for diffusion-based generative models, aiming to steer a base model toward high-reward outputs without updating its weights. Recent Sequential Mon…
Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction
Yiting He, Zhishuai Liu, Weixin Wang +1
Off-dynamics reinforcement learning (RL), where training and deployment transition dynamics are different, can be formulated as learning in a robust Markov decision process (RMDP)…
Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
Weixin Wang, Haoyang Zheng, Guang Lin +2
Langevin-based Thompson Sampling (TS) methods for multi-armed bandits use likelihood gradients to approximate posterior samples when exact sampling is intractable. As new rewards a…
Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits
Jiazheng Sun, Weixin Wang, Pan Xu
We provide a unified algorithmic framework for ensemble sampling in nonlinear contextual bandits and develop corresponding regret bounds for two most common nonlinear contextual ba…
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning
Zhishuai Liu, Weixin Wang, Pan Xu
We study off-dynamics Reinforcement Learning (RL), where the policy training and deployment environments are different. To deal with this environmental perturbation, we focus on le…
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
Hao-Lun Hsu, Weixin Wang, Miroslav Pajic +1
We present the first study on provably efficient randomized exploration in cooperative multi-agent reinforcement learning (MARL). We propose a unified algorithm framework for rando…