82 citations · 85 across the 14 of their papers we have counts for
7 papers · 1 filter
Projection-Free Algorithms for Nonsmooth Stochastic Convex-Concave Saddle-Point Problems
Khanh-Hung Giang-Tran, Soroosh Shafiee
We study nonsmooth convex-concave saddle-point problems over compact convex sets, assuming access to stochastic subgradients of the payoff function. We develop single-loop projecti…
Estimate then Predict: Convex Formulation for Travel Demand Forecasting
Youngseo Kim, Gioele Zardini, Samitha Samaranayake +1
Travel demand forecasting is essential for evaluating large-scale infrastructure projects, yet the traditional sequential four-step process can produce inconsistencies across trip…
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…
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…