5 papers
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.…
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…
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…
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…