PhD student, University of Illinois at Urbana-Champaign
2 papers at NeurIPS 2025
We propose the a theoretical-sound machine unlearning evaluation framework with provable properties.
Propose a scalable gradient compression algorithm for data attribution with sub-linear complexity that achieves competitive attribution results.
