13 papers
Oracle-Based Distributionally Robust Optimization under Optimal Transport Ambiguity Sets
Guixian Chen, Salar Fattahi, Soroosh Shafiee
Distributionally robust optimization (DRO) with optimal transport ambiguity sets is traditionally solved by reformulating the minimax problem into a single-level convex program. Wh…
Coordinate Optimality Reformulation for Mixed-Integer Convex Programs with Indicators
Tong Xu, Salar Fattahi, Andrés Gómez +1
We consider mixed-integer convex optimization problems in which binary indicators control continuous variables. We introduce the \emph{Coordinate Optimality Reformulation} (CORe) f…
Solving Convex Quadratic Optimization with Indicators Over Structured Graphs
Aaresh Bhathena, Salar Fattahi, Andrés Gómez +1
This paper studies convex quadratic minimization problems in which each continuous variable is coupled with a binary indicator variable. We focus on the structured setting where th…
Risk-Averse Wasserstein Distributionally Robust Online Learning
Guixian Chen, Salar Fattahi, Soroosh Shafiee
We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from a Wasserstein ambi…
Understanding the Implicit Regularization of Gradient Descent in Over-parameterized Models
Jianhao Ma, Geyu Liang, Salar Fattahi
Implicit regularization refers to the tendency of local search algorithms to converge to low-dimensional solutions, even when such structures are not explicitly enforced. Despite i…
Sparse Mean Estimation in Adversarial Settings via Incremental Learning
Jianhao Ma, Rui Ray Chen, Yinghui He +2
In this paper, we study the problem of sparse mean estimation under adversarial corruptions, where the goal is to estimate the -sparse mean of a heavy-tailed distribution from s…