41 citations · 158 across the 21 of their papers we have counts for
23 papers
Improving Diffusion-based Inverse Algorithms under Few-Step Constraint via Learnable Linear Extrapolation
Jiawei Zhang, Ziyuan Liu, Leon Yan +2
Diffusion-based inverse algorithms have shown remarkable performance across various inverse problems, yet their reliance on numerous denoising steps incurs high computational costs…
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…
Multi-Agent Reinforcement Learning with Reward Delays
Yuyang Zhang, Runyu Zhang, Yuantao Gu +1
This paper considers multi-agent reinforcement learning (MARL) where the rewards are received after delays and the delay time varies across agents and across time steps. Based on t…
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…
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps
Gen Li, Changxiao Cai, H. Vincent Poor +1
Eigenvector perturbation analysis plays a vital role in various data science applications. A large body of prior works, however, focused on establishing eigenvector pert…
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…
Subspace Estimation from Unbalanced and Incomplete Data Matrices: Statistical Guarantees
Changxiao Cai, Gen Li, Yuejie Chi +2
This paper is concerned with estimating the column space of an unknown low-rank matrix , given noisy and partial observatio…
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…
Lower Bound for RIP Constants and Concentration of Sum of Top Order Statistics
Gen Li, Xingyu Xu, Yuantao Gu
Restricted Isometry Property (RIP) is of fundamental importance in the theory of compressed sensing and forms the base of many exact and robust recovery guarantees in this field. A…
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces
Xingyu Xv, Gen Li, Yuantao Gu
Dimensionality reduction is a popular approach to tackle high-dimensional data with low-dimensional nature. Subspace Restricted Isometry Property, a newly-proposed concept, has pro…
Rigorous Restricted Isometry Property of Low-Dimensional Subspaces
Gen Li, Qinghua Liu, Yuantao Gu
Dimensionality reduction is in demand to reduce the complexity of solving large-scale problems with data lying in latent low-dimensional structures in machine learning and computer…
Linear Convergence of An Iterative Phase Retrieval Algorithm with Data Reuse
Gen Li, Yuchen Jiao, Yuantao Gu
Phase retrieval has been an attractive but difficult problem rising from physical science, and there has been a gap between state-of-the-art theoretical convergence analyses and th…
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…
Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces
Gen Li, Yuantao Gu
Dimension reduction plays an essential role when decreasing the complexity of solving large-scale problems. The well-known Johnson-Lindenstrauss (JL) Lemma and Restricted Isometry…
Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation
Yue M. Lu, Gen Li
We study a spectral initialization method that serves a key role in recent work on estimating signals in nonconvex settings. Previous analysis of this method focuses on the phase r…
Approximation of Gram-Schmidt Orthogonalization by Data Matrix
Gen Li, Yuantao Gu
For a matrix with linearly independent columns, this work studies to use its normalization and itself to approximate its orthonormalization $\bf V…