6 papers
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
Rui Ai, David Simchi-Levi, Feng Zhu
We study the pricing behavior of third-party platforms facing strategic agents. Assuming the platform is a revenue maximizer, it observes market features that generally affect dema…
Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
Rui Ai, Yuqi Pan, David Simchi-Levi +2
With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively aggregate answers from multiple LLMs has emerged as a fundamental challenge. Standar…
ShapE-GRPO: Shapley-Enhanced Reward Allocation for Multi-Candidate LLM Training
Rui Ai, Yu Pan, David Simchi-Levi +1
In user-agent interaction scenarios such as recommendation, brainstorming, and code suggestion, Large Language Models (LLMs) often generate sets of candidate recommendations where…
GenAI vs. Human Creators: Procurement Mechanism Design in Two-/Three-Layer Markets
Rui Ai, David Simchi-Levi, Haifeng Xu
With the rapid advancement of generative AI (GenAI), mechanism design adapted to its unique characteristics poses new theoretical and practical challenges. Unlike traditional goods…
Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving
Rui Ai, Hugo De Oliveira Barbalho, Sirui Li +3
A common challenge in real-time operations is deciding whether to re-solve an optimization problem or continue using an existing solution. While modern data platforms may collect i…
Contextual Online Decision Making with Infinite-Dimensional Functional Regression
Haichen Hu, Rui Ai, Stephen Bates +1
Contextual sequential decision-making problems play a crucial role in machine learning, encompassing a wide range of downstream applications such as bandits, sequential hypothesis…