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math.OC2025
Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points
Naoya Yamamoto, Juno Kim, Taiji Suzuki
Wasserstein gradient flow (WGF) is a common method to perform optimization over the space of probability measures. While WGF is guaranteed to converge to a first-order stationary p…
math.OC2023
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
Juno Kim, Kakei Yamamoto, Kazusato Oko +2
In this paper, we extend mean-field Langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates. We p…