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20222025
most citedHigh-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

11 citations · 12 across the 13 of their papers we have counts for

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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…