12 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…
Auto-Conditioned Frank-Wolfe Algorithms
Khanh-Hung Giang-Tran, Soroosh Shafiee, Nam Ho-Nguyen
Frank-Wolfe methods are projection-free algorithms for constrained optimization whose practical performance often depends critically on the choice of step size. Classical closed-lo…
A Saddle Point Algorithm for Robust Data-Driven Factor Model Problems
Shabnam Khodakaramzadeh, Soroosh Shafiee, Gabriel de Albuquerque Gleizer +1
We study the factor model problem, which aims to uncover low-dimensional structures in high-dimensional datasets. Adopting a robust data-driven approach, we formulate the problem a…
Projection-Free Algorithms for Minimax Problems
Khanh-Hung Giang-Tran, Soroosh Shafiee, Nam Ho-Nguyen
This paper addresses constrained smooth saddle-point problems in settings where projection onto the feasible sets is computationally expensive. We bridge the gap between projection…
GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal -sparse GLMs
Jiachang Liu, Andrea Lodi, Soroosh Shafiee
We investigate the problem of certifying optimality for sparse generalized linear models (GLMs), where sparsity is enforced through a cardinality constraint. While Branch-and-Bound…
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…