12 papers
Taylor Representations for Model-Free RL in Networked MDPs
Salah Chikhi, Abdelhaq Chaoui, Asuman Ozdaglar +1
In Networked Markov Decision Processes, transition dynamics are often unknown and the state--action space grows rapidly with the number of agents. In this setting, Taylor represent…
TACIT: Optimization Models that Learn from Their Mistakes
Maxime Bouscary, Marco Molinaro, Sirui Li +3
Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge, making them hard to formalize.…
Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion
Runyu Zhang, Jiawei Zhang, Gioele Zardini +2
Diffusion models are increasingly used not only for sampling from learned data distributions, but also for generating samples that optimize task-specific objectives. A common appro…
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…
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…
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…
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…
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…
Integrated Bundling and Pricing of Unique Items
Maxime Bouscary, Mazen Danaf, Saurabh Amin
Retailers have significant potential to improve recommendations through strategic bundling and pricing. By taking into account different types of customers and their purchasing dec…
Addressing misspecification in contextual optimization
Omar Bennouna, Jiawei Zhang, Saurabh Amin +1
We study a linear contextual optimization problem where a decision maker has access to historical data and contextual features to learn a cost prediction model aimed at minimizing…