Search Results for author: Kazusato Oko

Found 5 papers, 0 papers with code

Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems

no code implementations2 Dec 2023 Juno Kim, Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki

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.

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems

no code implementations6 Mar 2023 Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki

The entropic fictitious play (EFP) is a recently proposed algorithm that minimizes the sum of a convex functional and entropy in the space of measures -- such an objective naturally arises in the optimization of a two-layer neural network in the mean-field regime.

Image Generation

Diffusion Models are Minimax Optimal Distribution Estimators

no code implementations3 Mar 2023 Kazusato Oko, Shunta Akiyama, Taiji Suzuki

While efficient distribution learning is no doubt behind the groundbreaking success of diffusion modeling, its theoretical guarantees are quite limited.

Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning

no code implementations1 Sep 2022 Kazusato Oko, Shunta Akiyama, Tomoya Murata, Taiji Suzuki

While variance reduction methods have shown great success in solving large scale optimization problems, many of them suffer from accumulated errors and, therefore, should periodically require the full gradient computation.

Federated Learning

Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization

no code implementations ICLR 2022 Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu

We introduce Particle-SDCA, a gradient-based optimization algorithm for two-layer neural networks in the mean field regime that achieves exponential convergence rate in regularized empirical risk minimization.

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