V
Vishal Gupta
Researcher at University of Southern California
Publications - 26
Citations - 1669
Vishal Gupta is an academic researcher from University of Southern California. The author has contributed to research in topics: Robust optimization & Estimator. The author has an hindex of 13, co-authored 26 publications receiving 1249 citations. Previous affiliations of Vishal Gupta include Massachusetts Institute of Technology.
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Data-driven robust optimization
TL;DR: This work proposes a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests, and shows that data-driven sets significantly outperform traditional robust optimization techniques whenever data is available.
Data-driven robust optimization
TL;DR: In this paper, the authors propose a data-driven approach for robust optimization using statistical hypothesis tests, which is flexible and widely applicable, and robust optimization problems built from their new sets are computationally tractable, both theoretically and practically.
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Data-driven estimation in equilibrium using inverse optimization
TL;DR: This work develops an efficient, data-driven technique for estimating the parameters of these models from observed equilibria, and supports both parametric and nonparametric estimation by leveraging ideas from statistical learning (kernel methods and regularization operators).
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Robust sample average approximation
TL;DR: In this article, the authors propose a modification of SAA called robust SAA, which retains SAA's tractability and asymptotic properties and enjoys strong finite-sample performance guarantees.
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Robust Sample Average Approximation
TL;DR: This paper proposes a modification of SAA, which retains SAA’s tractability and asymptotic properties and, additionally, enjoys strong finite-sample performance guarantees, and presents examples from inventory management and portfolio allocation, demonstrating numerically that this approach outperforms other data-driven approaches in these applications.