9 papers
Learning Decision-Sufficient Representations for Linear Optimization
Yuhan Ye, Saurabh Amin, Asuman Ozdaglar
We study how to construct compressed datasets that suffice to recover optimal decisions in linear programs with an unknown cost vector lying in a prior set . Recen…
Budget-Efficient Automatic Algorithm Design via Code Graph
Maxime Bouscary, Manxi Wu, Saurabh Amin
Large language models (LLMs) have emerged as powerful tools for automatic algorithm design (AAD). However, existing pipelines remain inefficient. They operate at the granularity of…
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…
OptiHive: Ensemble Selection for LLM-Based Optimization via Statistical Modeling
Maxime Bouscary, Saurabh Amin
LLM-based solvers have emerged as a promising means of automating problem modeling and solving. However, they remain unreliable and often depend on iterative repair loops that resu…
Reducing Contextual Stochastic Bilevel Optimization via Structured Function Approximation
Maxime Bouscary, Jiawei Zhang, Saurabh Amin
Contextual Stochastic Bilevel Optimization (CSBO) extends standard stochastic bilevel optimization (SBO) by incorporating context-dependent lower-level problems. CSBO problems are…