REU Project

This project studies whether top-(k) rankings can be trusted when many machine learning models achieve nearly identical predictive performance. Rather than analyzing a single trained model, we examine the surrounding Rashomon set of near-optimal models. We develop optimization methods to track how each item's score, rank, and top-(k) membership can change across plausible models. These tools help identify items that are consistently highly ranked, items whose positions are fundamentally uncertain, and comparisons that can reverse without meaningfully affecting model accuracy. The project combines machine learning, mathematical optimization, and algorithm design to create interpretable diagnostics for ranking reliability in applications such as recommender systems, hiring tools, search engines, and LLM response ranking.