61 citations · 85 across the 12 of their papers we have counts for
13 papers
Overcoming Demand Censoring through Randomization for Risk-Sensitive Newsvendor Learning
Zhengchao Wang, Nam Ho-Nguyen
We study online newsvendor learning with bounded demand, censored observations, and convex nonlinear shortage costs. Because stockouts can conceal the shortage magnitudes needed by…
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…
Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs
Hoyeol Yoon, Seoungbin Bae, Nam Ho-Nguyen +1
Physics-informed neural networks (PINNs) are a promising approach for solving partial differential equations (PDEs). Their training, however, is often difficult because multiple lo…
Convergence, Duality and Well-Posedness in Convex Bilevel Optimization
Khanh-Hung Giang-Tran, Nam Ho-Nguyen, Fatma Kılınç-Karzan +1
We consider the convex bilevel optimization problem, also known as simple bilevel programming. There are two challenges in solving convex bilevel optimization problems. Firstly, st…
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…
Mistake, Manipulation and Margin Guarantees in Online Strategic Classification
Lingqing Shen, Nam Ho-Nguyen, Khanh-Hung Giang-Tran +1
We consider an online strategic classification problem where each arriving agent can manipulate their true feature vector to obtain a positive predicted label, while incurring a co…
A Projection-Free Method for Solving Convex Bilevel Optimization Problems
Khanh-Hung Giang-Tran, Nam Ho-Nguyen, Dabeen Lee
When faced with multiple minima of an "inner-level" convex optimization problem, the convex bilevel optimization problem selects an optimal solution which also minimizes an auxilia…
Projection-Free Online Convex Optimization with Stochastic Constraints
Duksang Lee, Nam Ho-Nguyen, Dabeen Lee
This paper develops projection-free algorithms for online convex optimization with stochastic constraints. We design an online primal-dual projection-free framework that can take a…
Non-Smooth, Hölder-Smooth, and Robust Submodular Maximization
Duksang Lee, Nam Ho-Nguyen, Dabeen Lee
We study the problem of maximizing a continuous DR-submodular function that is not necessarily smooth. We prove that the continuous greedy algorithm achieves an $[(1-1/e)\OPT-ε]$ g…
Exploiting Problem Structure in Optimization under Uncertainty via Online Convex Optimization
Nam Ho-Nguyen, Fatma Kilinc-Karzan
In this paper, we consider two paradigms that are developed to account for uncertainty in optimization models: robust optimization (RO) and joint estimation-optimization (JEO). We…
Dynamic Data-Driven Estimation of Non-Parametric Choice Models
Nam Ho-Nguyen, Fatma Kilinc-Karzan
We study non-parametric estimation of choice models, which were introduced to alleviate unreasonable assumptions in traditional parametric models, and are prevalent in several appl…
Online First-Order Framework for Robust Convex Optimization
Nam Ho-Nguyen, Fatma Kilinc-Karzan
Robust optimization (RO) has emerged as one of the leading paradigms to efficiently model parameter uncertainty. The recent connections between RO and problems in statistics and ma…
A Second-Order Cone Based Approach for Solving the Trust Region Subproblem and Its Variants
Nam Ho-Nguyen, Fatma Kilinc-Karzan
We study the trust-region subproblem (TRS) of minimizing a nonconvex quadratic function over the unit ball with additional conic constraints. Despite having a nonconvex objective,…