28 citations · 52 across the 6 of their papers we have counts for
8 papers
Balanced Off-Policy Evaluation for Personalized Pricing
Adam N. Elmachtoub, Vishal Gupta, Yunfan Zhao
We consider a personalized pricing problem in which we have data consisting of feature information, historical pricing decisions, and binary realized demand. The goal is to perform…
Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization
Vishal Gupta, Michael Huang, Paat Rusmevichientong
Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven op…
Data-Pooling in Stochastic Optimization
Vishal Gupta, Nathan Kallus
Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data. Intuition suggests one can decou…
Robust Sample Average Approximation
Dimitris Bertsimas, Vishal Gupta, Nathan Kallus
Sample average approximation (SAA) is a widely popular approach to data-driven decision-making under uncertainty. Under mild assumptions, SAA is both tractable and enjoys strong as…
A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation
Dimitris Bertsimas, J. Daniel Griffith, Vishal Gupta +3
Dynamic resource allocation (DRA) problems are an important class of dynamic stochastic optimization problems that arise in a variety of important real-world applications. DRA prob…
Data-Driven Robust Optimization
Dimitris Bertsimas, Vishal Gupta, Nathan Kallus
The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utili…