Full Professor, University of Illinois at Urbana-Champaign
4 papers at NeurIPS 2025
A new learning framework that improves LLM inference by learning from a Mistake Log collected during fine-tuning.
We introduce CLIMB, a comprehensive benchmark and empirical study of 29 class-imbalanced learning methods on 73 real-world tabular datasets, revealing key insights into method performance, efficiency, and robustness.
