Stefanie Jegelka

We develop algorithms that are guaranteed to PAC learn transformers.

We introduce a systematic approach to flexibly incorporating representation guidance into diffusion models, resulting in both accelerated training and better performance across image, protein, and molecule generation tasks.

We present an end-to-end framework for self supervised combinatorial optimization under various constraints (cardinality, matroid, independent set, etc.).