6 papers
Statistical Inference for Policy Evaluation with Temporal Difference Learning
Weichen Wu, Gen Li, Yuting Wei +1
We investigate the statistical properties of Temporal Difference (TD) learning with Polyak-Ruppert averaging, arguably one of the most widely used algorithms in reinforcement learn…
Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality
Zhihan Huang, Yuting Wei, Yuxin Chen
The denoising diffusion probabilistic model (DDPM) has emerged as a mainstream generative model in generative AI. While sharp convergence guarantees have been established for the D…
Transformers Meet In-Context Learning: A Universal Approximation Theory
Gen Li, Yuchen Jiao, Yu Huang +2
Large language models are capable of in-context learning, the ability to perform new tasks at test time using a handful of input-output examples, without parameter updates. We deve…
Faster Diffusion Models via Higher-Order Approximation
Gen Li, Yuchen Zhou, Yuting Wei +1
In this paper, we explore provable acceleration of diffusion models without any additional retraining. Focusing on the task of approximating a target data distribution in $\mathbb{…
Statistical and Algorithmic Foundations of Reinforcement Learning
Yuejie Chi, Yuxin Chen, Yuting Wei
As a paradigm for sequential decision making in unknown environments, reinforcement learning (RL) has received a flurry of attention in recent years. However, the explosion of mode…
Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures
Gen Li, Changxiao Cai, Yuting Wei
Diffusion models are distinguished by their exceptional generative performance, particularly in producing high-quality samples through iterative denoising. While current theory sug…