PhD student, University of California, Irvine
2 papers at NeurIPS 2025
We consider the computational complexity of computing stationary points in min-max optimization, with a particular focus on the special case of computing Nash equilibria in (two-)team zero-sum games.
We investigate the computational complexity of finding local solutions for many contrastive learning settings based on triplet constraints (anchor-positive-negative paradigm), and we prove that reaching local optima cannot be done in polynomial time.
