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J-GLOBAL ID:201801013267468381   Update date: Mar. 19, 2024

Nitanda Atsushi

Nitanda Atsushi
Affiliation and department:
Job title: Principal Scientist
Homepage URL  (1): https://sites.google.com/site/atsushinitanda
Research field  (1): Mathematical informatics
Research keywords  (3): Deep Learning ,  Stochastic Optimization ,  Machine Learning
Research theme for competitive and other funds  (3):
  • 2022 - 2026 Development of adaptive leanring method based on optimization of probability measures
  • 2019 - 2023 深層学習の潜在的正則構造の理解に基づく学習法の安定化と高速化
  • 2019 - 2021 超高次元機械学習モデルの学習ダイナミクスの究明と効率的学習法の開発
Papers (25):
  • Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu. Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. International Conference on Learning Representations. 2022
  • Atsushi Nitanda, Denny Wu, Taiji Suzuki. Convex Analysis of the Mean Field Langevin Dynamics. International Conference on Artificial Intelligence and Statistics. 2022
  • Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza. Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. Neural Information Processing Systems. 2021
  • Atsushi Nitanda, Denny Wu, Taiji Suzuki. Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. Neural Information Processing Systems. 2021
  • Taiji Suzuki, Atsushi Nitanda. Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. Neural Information Processing Systems. 2021
more...
MISC (9):
  • 二反田篤史. ニューラルネットワークの最適化理論. オペレーションズ・リサーチ. 2020
  • 二反田篤史. 確率的最適化法の収束解析. 第32回RAMPシンポジウム論文集. 2020
  • 二反田篤史. 確率的勾配降下法とニューラルネットワーク. 数理科学「統計的思考法のすすめ」. 2020
  • 鈴木大慈, 二反田篤史, 村田智也. 機械学習問題における確率的最適化技法. オペレーションズ・リサーチ. 2019
  • 鈴木大慈, 二反田篤史, 村田智也. 構造のある機械学習問題における最適化技法. RAMPシンポジウム. 2017
more...
Patents (1):
  • 貯蔵タンク運用計画導出システム及び方法
Books (1):
  • 理論計算機科学事典
    朝倉書店 2022
Lectures and oral presentations  (49):
  • Convex Analysis of the Mean Field Langevin Dynamics
    (Workshop on Functional Inference and Machine Intelligence 2022)
  • Efficient optimization methods for two-layer neural networks in mean-field regime
    (CREST-Deep meeting 2021)
  • 平均場ニューラルネットワークの効率的最適化
    (AI数理セミナー 2021)
  • Particle Stochastic Dual Coordinate Ascent: Exponential Convergent Algorithm for Mean Field Neural Network Optimization
    (情報論的学習理論ワークショップ 2021)
  • 平均場ニューラルネットワークの効率的最適化法
    (統計関連学会連合大会 2021)
more...
Education (2):
  • 2017 - 2018 The University of Tokyo The Graduate School of Information Science and Technology Department of Mathematical Informatics
  • 2007 - 2009 The University of Tokyo
Professional career (1):
  • 博士(情報理工学) (東京大学)
Work history (8):
  • 2024/01 - 現在 A*STAR
  • 2021/04 - 2023/12 Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Associate Professor
  • 2018/12 - 2023/12 RIKEN AIP Visiting Researcher
  • 2019/10 - 2023/03 JST PRESTO Researcher
  • 2018/10 - 2021/03 The University of Tokyo The Graduate School of Information Science and Technology
Show all
Awards (5):
  • 2021/04 - The Ninth International Conference on Learning Representations Outstanding Paper Award Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
  • 2019/11 - IEEE International Conference on Data Mining ICDM '19 Best Paper Candidate for KAIS Publication Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization
  • 2019/11 - 第22回情報論的学習理論ワークショップ(IBIS2019) ベストプレゼンテーション賞 SGDの挙動解析に基づくデータクレンジング
  • 2019/03 - Graduate School of Information Science and Technology, The University of Tokyo Dean's Award
  • 2009/03 - Graduate School of Mathematical Sciences, The University of Tokyo Dean's Award
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