41 citations · 158 across the 21 of their papers we have counts for
12 papers · 1 filter
Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems
Jiawei Zhang, Jiaxin Zhuang, Cheng Jin +2
The recent emergence of diffusion models has significantly advanced the precision of learnable priors, presenting innovative avenues for addressing inverse problems. Since inverse…
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Gen Li, Laixi Shi, Yuxin Chen +1
Achieving sample efficiency in online episodic reinforcement learning (RL) requires optimally balancing exploration and exploitation. When it comes to a finite-horizon episodic Mar…
The Rate of Convergence of Variation-Constrained Deep Neural Networks
Gen Li, Jie Ding
Multi-layer feedforward networks have been used to approximate a wide range of nonlinear functions. An important and fundamental problem is to understand the learnability of a netw…
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Gen Li, Yuxin Chen, Yuejie Chi +2
Low-complexity models such as linear function representation play a pivotal role in enabling sample-efficient reinforcement learning (RL). The current paper pertains to a scenario…
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
Gen Li, Yuting Wei, Yuejie Chi +1
The softmax policy gradient (PG) method, which performs gradient ascent under softmax policy parameterization, is arguably one of the de facto implementations of policy optimizatio…
The Efficacy of Regularization in Two-Layer Neural Networks
Gen Li, Yuantao Gu, Jie Ding
A crucial problem in neural networks is to select the most appropriate number of hidden neurons and obtain tight statistical risk bounds. In this work, we present a new perspective…
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
Gen Li, Yuting Wei, Yuejie Chi +2
Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP), based on a single trajectory of Markovian samples induce…
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
Gen Li, Yuting Wei, Yuejie Chi +1
This paper is concerned with the sample efficiency of reinforcement learning, assuming access to a generative model (or simulator). We first consider -discounted infinite-horizo…
Nonconvex Low-Rank Tensor Completion from Noisy Data
Changxiao Cai, Gen Li, H. Vincent Poor +1
We study a noisy tensor completion problem of broad practical interest, namely, the reconstruction of a low-rank tensor from highly incomplete and randomly corrupted observations o…
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
Gen Li, Yuantao Gu
Spectral Method is a commonly used scheme to cluster data points lying close to Union of Subspaces by first constructing a Random Geometry Graph, called Subspace Clustering. This p…
Compressed Subspace Learning Based on Canonical Angle Preserving Property
Yuchen Jiao, Gen Li, Yuantao Gu
Union of Subspaces (UoS) is a popular model to describe the underlying low-dimensional structure of data. The fine details of UoS structure can be described in terms of canonical a…
Active Orthogonal Matching Pursuit for Sparse Subspace Clustering
Yanxi Chen, Gen Li, Yuantao Gu
Sparse Subspace Clustering (SSC) is a state-of-the-art method for clustering high-dimensional data points lying in a union of low-dimensional subspaces. However, while opt…