Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. - Kazusato Oko; Taiji Suzuki; Atsushi Nitanda; Denny Wu (ICLR, 2022)

MOCHA: mobile check-in application for university campuses beyond COVID-19. - Yuuki Nishiyama; Hiroaki Murakami; Ryoto Suzuki; Kazusato Oko; Issey Sukeda; Kaoru Sezaki; Yoshihiro Kawahara (MobiHoc, 2022)

Nearly Tight Spectral Sparsification of Directed Hypergraphs by a Simple Iterative Sampling Algorithm. - Kazusato Oko; Shinsaku Sakaue; Shin-ichi Tanigawa (CoRR, 2022)

Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning. - Kazusato Oko; Shunta Akiyama; Tomoya Murata; Taiji Suzuki (CoRR, 2022)

Nearly Tight Spectral Sparsification of Directed Hypergraphs. - Kazusato Oko; Shinsaku Sakaue; Shin-ichi Tanigawa (ICALP, 2023)

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. - Atsushi Nitanda; Kazusato Oko; Denny Wu; Nobuhito Takenouchi; Taiji Suzuki (ICML, 2023)

Diffusion Models are Minimax Optimal Distribution Estimators. - Kazusato Oko; Shunta Akiyama; Taiji Suzuki (ICML, 2023)

Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. - Taiji Suzuki; Denny Wu; Kazusato Oko; Atsushi Nitanda (NeurIPS, 2023)

Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. - Kazusato Oko; Yujin Song; Taiji Suzuki; Denny Wu (COLT, 2024)

Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems. - Juno Kim; Kakei Yamamoto; Kazusato Oko; Zhuoran Yang; Taiji Suzuki (ICLR, 2024)

Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. - Atsushi Nitanda; Kazusato Oko; Taiji Suzuki; Denny Wu (ICLR, 2024)

SILVER: Single-loop variance reduction and application to federated learning. - Kazusato Oko; Shunta Akiyama; Denny Wu; Tomoya Murata; Taiji Suzuki (ICML, 2024)

Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning. - Kakei Yamamoto; Kazusato Oko; Zhuoran Yang; Taiji Suzuki (ICML, 2024)

Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. - Jason D. Lee; Kazusato Oko; Taiji Suzuki; Denny Wu (NeurIPS, 2024)

Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context. - Kazusato Oko; Yujin Song; Taiji Suzuki; Denny Wu (NeurIPS, 2024)

Flow matching achieves minimax optimal convergence. - Kenji Fukumizu; Taiji Suzuki; Noboru Isobe; Kazusato Oko; Masanori Koyama (CoRR, 2024)

Flow matching achieves almost minimax optimal convergence. - Kenji Fukumizu; Taiji Suzuki; Noboru Isobe; Kazusato Oko; Masanori Koyama (ICLR, 2025)

Direct Distributional Optimization for Provable Alignment of Diffusion Models. - Ryotaro Kawata; Kazusato Oko; Atsushi Nitanda; Taiji Suzuki (ICLR, 2025)

A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI. - Kazusato Oko; Licong Lin; Yuhang Cai; Song Mei (CoRR, 2025)

When Does Metadata Conditioning (NOT) Work for Language Model Pre-Training? A Study with Context-Free Grammars. - Rei Higuchi; Ryotaro Kawata; Naoki Nishikawa; Kazusato Oko; Shoichiro Yamaguchi; Sosuke Kobayashi; Seiya Tokui; Kohei Hayashi; Daisuke Okanohara; Taiji Suzuki (CoRR, 2025)