3 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
Incremental Task Learning with Incremental Rank Updates
Rakib Hyder, Ken Shao, Boyu Hou +3
Incremental Task learning (ITL) is a category of continual learning that seeks to train a single network for multiple tasks (one after another), where training data for each task i…