3 citations · 3 across the 9 of their papers we have counts for
9 papers
MG-SpaIR: Multi-grade Sparse-guided Implicit Representation for Training-Data-Free Image Restoration
Jianmin Liao, Lei Huang, Ronglong Fang +3
MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building o…
Signal and Image Recovery with Scale and Signed Permutation Invariant Sparsity-Promoting Functions
Jianqing Jia, Ashley Prater-Bennette, Lixin Shen
Sparse signal recovery has been a cornerstone of advancements in data processing and imaging. Recently, the squared ratio of to norms, , has be…
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
Qi Zhang, Yi Zhou, Ashley Prater-Bennette +2
Distributionally robust optimization (DRO) is a powerful framework for training robust models against data distribution shifts. This paper focuses on constrained DRO, which has an…
Sparse Recovery: The Square of Norms
Jianqing Jia, Ashley Prater-Bennette, Lixin Shen +1
This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel model termed the -model, which utilizes the squared norms for this pur…
Computing Proximity Operators of Scale and Signed Permutation Invariant Functions
Jianqing Jia, Ashley Prater-Bennette, Lixin Shen
This paper investigates the computation of proximity operators for scale and signed permutation invariant functions. A scale-invariant function remains unchanged under uniform scal…
Model-Free Robust Average-Reward Reinforcement Learning
Yue Wang, Alvaro Velasquez, George Atia +2
Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In this paper, we focus…
Robust Average-Reward Markov Decision Processes
Yue Wang, Alvaro Velasquez, George Atia +2
In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty…
The Proximity Operator of the Log-Sum Penalty
Ashley Prater-Bennette, Lixin Shen, Erin E. Tripp
The log-sum penalty is often adopted as a replacement for the pseudo-norm in compressive sensing and low-rank optimization. The hard-thresholding operator, i.e., the proxi…
Sparse tensor recovery via N-mode FISTA with support augmentation
Ashley Prater-Bennette, Lixin Shen
A common approach for performing sparse tensor recovery is to use an N-mode FISTA method. However, this approach may fail in some cases by missing some values in the true support o…