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Kazusato Oko
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2020 – today
- 2024
- [c11]Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu:
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. COLT 2024: 4009-4081 - [c10]Juno Kim, Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki:
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems. ICLR 2024 - [c9]Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu:
Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. ICLR 2024 - [c8]Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki:
SILVER: Single-loop variance reduction and application to federated learning. ICML 2024 - [c7]Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki:
Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning. ICML 2024 - [i7]Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama:
Flow matching achieves minimax optimal convergence. CoRR abs/2405.20879 (2024) - [i6]Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu:
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. CoRR abs/2406.01581 (2024) - [i5]Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu:
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations. CoRR abs/2406.11828 (2024) - 2023
- [c6]Kazusato Oko, Shinsaku Sakaue, Shin-ichi Tanigawa:
Nearly Tight Spectral Sparsification of Directed Hypergraphs. ICALP 2023: 94:1-94:19 - [c5]Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki:
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. ICML 2023: 26266-26282 - [c4]Kazusato Oko, Shunta Akiyama, Taiji Suzuki:
Diffusion Models are Minimax Optimal Distribution Estimators. ICML 2023: 26517-26582 - [c3]Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda:
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. NeurIPS 2023 - [i4]Kazusato Oko, Shunta Akiyama, Taiji Suzuki:
Diffusion Models are Minimax Optimal Distribution Estimators. CoRR abs/2303.01861 (2023) - [i3]Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki:
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. CoRR abs/2303.02957 (2023) - 2022
- [c2]Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu:
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. ICLR 2022 - [c1]Yuuki Nishiyama, Hiroaki Murakami, Ryoto Suzuki, Kazusato Oko, Issey Sukeda, Kaoru Sezaki, Yoshihiro Kawahara:
MOCHA: mobile check-in application for university campuses beyond COVID-19. MobiHoc 2022: 253-258 - [i2]Kazusato Oko, Shinsaku Sakaue, Shin-ichi Tanigawa:
Nearly Tight Spectral Sparsification of Directed Hypergraphs by a Simple Iterative Sampling Algorithm. CoRR abs/2204.02537 (2022) - [i1]Kazusato Oko, Shunta Akiyama, Tomoya Murata, Taiji Suzuki:
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning. CoRR abs/2209.00361 (2022)
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