5 papers
Data Informativeness in Linear Optimization under Uncertainty
Omar Bennouna, Amine Bennouna, Saurabh Amin +1
We study the problem of determining what data is required to solve a decision-making task when only partial information about the state of the world is available. Focusing on linea…
A Bayesian Framework for Human-AI Collaboration: Complementarity and Correlation Neglect
Saurabh Amin, Amine Bennouna, Daniel Huttenlocher +3
We develop a decision-theoretic model of human-AI interaction to study when AI assistance improves or impairs human decision-making. A human decision-maker observes private informa…
What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization
Omar Bennouna, Amine Bennouna, Saurabh Amin +1
We study the fundamental question of how informative a dataset is for solving a given decision-making task. In our setting, the dataset provides partial information about unknown p…
Holistic Robust Data-Driven Decisions
Amine Bennouna, Bart Van Parys, Ryan Lucas
The design of data-driven formulations for machine learning and decision-making with good out-of-sample performance is a key challenge. The observation that good in-sample performa…
From Distributional Robustness to Robust Statistics: A Confidence Sets Perspective
Gabriel Chan, Bart Van Parys, Amine Bennouna
We establish a connection between distributionally robust optimization (DRO) and classical robust statistics. We demonstrate that this connection arises naturally in the context of…