28 citations · 52 across the 4 of their papers we have counts for
6 papers
Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model
Nathan Kallus
Policy alignment to preference data typically assumes a known link function between observed preferences and latent rewards (e.g., Bradley-Terry model / logistic link). Misspecific…
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…
The Power and Limits of Predictive Approaches to Observational-Data-Driven Optimization
Dimitris Bertsimas, Nathan Kallus
While data-driven decision-making is transforming modern operations, most large-scale data is of an observational nature, such as transactional records. These data pose unique chal…
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…
From Predictive to Prescriptive Analytics
Dimitris Bertsimas, Nathan Kallus
In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using…
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…