Assistant Professor, State University of New York at Stony Brook
3 papers at NeurIPS 2025
Quantile-Guided Alignment (QA) is a framework for multi-dimensional quantile alignment that reduces catastrophic risks in language models by imposing constraints on reward quantiles across multiple performance dimensions.
A novel benchmark using a comprehensive preference dataset to evaluate multimodal judges across multiple key perspectives
