Full Professor, Massachusetts Institute of Technology
2 papers at NeurIPS 2025
A new segmentation framework and sampling mechanism to produce multiple consistent label maps across similar images on held-out datasets, outperforming baselines by a wide margin.
We propose a new evaluation method that makes use of three sources of information (unlabeled data, multiple classifiers, and probabilistic classifier scores) to produce more accurate performance estimates than prior work.
