306 citations · 1.3k across the 52 of their papers we have counts for
55 papers
Integrated Learning and Robust Optimization
Cheng Tan, Yuchen Mao, Shuming Wang +1
Many operational decisions require solving a linear program whose cost vector is unknown at decision time and must be predicted from contextual information. Because prediction and…
Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
Huangyu Xu, Jingqin Yang, Qianqian Xu +1
Sparse optimization is a fundamental challenge in various practical applications. A popular approach to sparse optimization is regularization. However, it may encounter op…
Robust Out-of-Distribution Stochastic Optimization
Xianyu Li, Huan Xu, Xiaolin Huang +1
Data-driven decision-making under uncertainty typically presumes the collection of historical data from an unknown target probability distribution. However, one may have no access…
Adversaries in Online Learning Revisited: with applications in Robust Optimization and Adversarial training
Sebastian Pokutta, Huan Xu
We revisit the concept of "adversary" in online learning, motivated by solving robust optimization and adversarial training using online learning methods. While one of the classica…
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Yan Li, Ethan X. Fang, Huan Xu +1
Adversarial training is a principled approach for training robust neural networks. Despite of tremendous successes in practice, its theoretical properties still remain largely unex…
Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs
Adrian Rivera Cardoso, Jacob Abernethy, He Wang +1
We study the problem of repeated play in a zero-sum game in which the payoff matrix may change, in a possibly adversarial fashion, on each round; we call these Online Matrix Games.…