82 citations · 85 across the 14 of their papers we have counts for
17 papers
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…
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…
Conditional Gradient Methods with Standard LMO for Stochastic Simple Bilevel Optimization
Khanh-Hung Giang-Tran, Soroosh Shafiee, Nam Ho-Nguyen
We propose efficient methods for solving stochastic simple bilevel optimization problems with convex inner levels, where the goal is to minimize an outer stochastic objective funct…
Scalable First-order Method for Certifying Optimal k-Sparse GLMs
Jiachang Liu, Soroosh Shafiee, Andrea Lodi
This paper investigates the problem of certifying optimality for sparse generalized linear models (GLMs), where sparsity is enforced through an cardinality constraint. Whi…
Distributionally Robust Optimization
Daniel Kuhn, Soroosh Shafiee, Wolfram Wiesemann
Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncer…
Robust Distribution Learning with Local and Global Adversarial Corruptions
Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee
We consider learning in an adversarial environment, where an -fraction of samples from a distribution are arbitrarily modified (global corruptions) and the remaini…
A Robust Optimization Approach to Network Control Using Local Information Exchange
Georgios Darivianakis, Angelos Georghiou, Soroosh Shafiee +1
Designing policies for a network of agents is typically done by formulating an optimization problem where each agent has access to state measurements of all the other agents in the…
Outlier-Robust Wasserstein DRO
Sloan Nietert, Ziv Goldfeld, Soroosh Shafiee
Distributionally robust optimization (DRO) is an effective approach for data-driven decision-making in the presence of uncertainty. Geometric uncertainty due to sampling or localiz…
Constrained Optimization of Rank-One Functions with Indicator Variables
Soroosh Shafiee, Fatma Kılınç-Karzan
Optimization problems involving minimization of a rank-one convex function over constraints modeling restrictions on the support of the decision variables emerge in various machine…
Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
Soroosh Shafiee, Liviu Aolaritei, Florian Dörfler +1
We study optimal transport-based distributionally robust optimization problems where a fictitious adversary, often envisioned as nature, can choose the distribution of the uncertai…
Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning
Liviu Aolaritei, Soroosh Shafiee, Florian Dörfler
Distributionally robust optimization (DRO) has become a powerful framework for estimation under uncertainty, offering strong out-of-sample performance and principled regularization…