N
Nathan Kallus
Researcher at Cornell University
Publications - 159
Citations - 4414
Nathan Kallus is an academic researcher from Cornell University. The author has contributed to research in topics: Computer science & Estimator. The author has an hindex of 31, co-authored 131 publications receiving 2968 citations. Previous affiliations of Nathan Kallus 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.
Journal ArticleDOI
From Predictive to Prescriptive Analytics
Dimitris Bertsimas,Nathan Kallus +1 more
TL;DR: The authors combine machine learning and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisi, for optimal decision making.
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.
Proceedings ArticleDOI
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
TL;DR: This paper decomposes the biases in estimating outcome disparity via threshold-based imputation into multiple interpretable bias sources, allowing us to explain when over- or underestimation occurs and proposes an alternative weighted estimator that uses soft classification.
Journal ArticleDOI
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.