PhD student, University of California, San Diego
2 papers at NeurIPS 2025
We introduce the Birkhoff Extension, a polynomial-time, almost-everywhere differentiable relaxation of permutation objectives to doubly stochastic matrices, enabling efficient combinatorial optimization.
We present an end-to-end framework for self supervised combinatorial optimization under various constraints (cardinality, matroid, independent set, etc.).
