Atsushi Nitanda

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 50

Top Topics

Classification Error

Fictitious Play

Stochastic Gradient

Top Venues

Publications

Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki
Koopman-based generalization bound: New aspect for full-rank weights. ICLR (2024)
Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu
Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. ICLR (2024)
Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu
Why is parameter averaging beneficial in SGD? An objective smoothing perspective. AISTATS (2024)
Atsushi Nitanda
Improved Particle Approximation Error for Mean Field Neural Networks. CoRR (2024)
Taiji Suzuki, Denny Wu, Atsushi Nitanda
Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. NeurIPS (2023)
Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi
Tight and fast generalization error bound of graph embedding in metric space. ICML (2023)
Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi
Tight and fast generalization error bound of graph embedding in metric space. CoRR (2023)
Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. CoRR (2023)
Taiji Suzuki, Atsushi Nitanda, Denny Wu
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. ICLR (2023)
Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda
Parameter Averaging for SGD Stabilizes the Implicit Bias towards Flat Regions. CoRR (2023)
Taiji Suzuki, Denny Wu, Atsushi Nitanda
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. CoRR (2023)
Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. NeurIPS (2023)
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki
Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering. CoRR (2023)
Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. ICML (2023)
Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. ICLR (2022)
Atsushi Nitanda, Denny Wu, Taiji Suzuki
Convex Analysis of the Mean Field Langevin Dynamics. AISTATS (2022)
Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda, Denny Wu
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime. NeurIPS (2022)
Atsushi Nitanda, Denny Wu, Taiji Suzuki
Convex Analysis of the Mean Field Langevin Dynamics. CoRR (2022)
Yuto Mori, Atsushi Nitanda, Akiko Takeda
BODAME: Bilevel Optimization for Defense Against Model Extraction. CoRR (2021)
Atsushi Nitanda, Denny Wu, Taiji Suzuki
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. NeurIPS (2021)
Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Marc Cavazza, Kenji Yamanishi
Generalization Error Bound for Hyperbolic Ordinal Embedding. CoRR (2021)
Atsushi Nitanda, Tomoya Murata, Taiji Suzuki
Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization. Knowl. Inf. Syst. 63 (9) (2021)
Atsushi Nitanda, Taiji Suzuki
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. ICLR (2021)
Shingo Yashima, Atsushi Nitanda, Taiji Suzuki
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. AISTATS (2021)
Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
When does preconditioning help or hurt generalization? ICLR (2021)
Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. NeurIPS (2021)
Taiji Suzuki, Atsushi Nitanda
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. NeurIPS (2021)
Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza
Generalization Error Bound for Hyperbolic Ordinal Embedding. ICML (2021)
Shintaro Fukushima, Atsushi Nitanda, Kenji Yamanishi
Online Robust and Adaptive Learning from Data Streams. CoRR (2020)
Atsushi Nitanda, Denny Wu, Taiji Suzuki
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. CoRR (2020)
Atsushi Nitanda, Taiji Suzuki
Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. AISTATS (2020)
Shun-ichi Amari, Jimmy Ba, Roger B. Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
When Does Preconditioning Help or Hurt Generalization? CoRR (2020)
Atsushi Nitanda, Taiji Suzuki
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. CoRR (2020)
Linchuan Xu, Jun Huang, Atsushi Nitanda, Ryo Asaoka, Kenji Yamanishi
A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification. CoRR (2020)
Satoshi Hara, Atsushi Nitanda, Takanori Maehara
Data Cleansing for Models Trained with SGD. CoRR (2019)
Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi
Hyperbolic Ordinal Embedding. ACML (2019)
Satoshi Hara, Atsushi Nitanda, Takanori Maehara
Data Cleansing for Models Trained with SGD. NeurIPS (2019)
Shingo Yashima, Atsushi Nitanda, Taiji Suzuki
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. CoRR (2019)
Atsushi Nitanda, Taiji Suzuki
Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems. CoRR (2019)
Atsushi Nitanda, Tomoya Murata, Taiji Suzuki
Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization. ICDM (2019)
Taiji Suzuki, Atsushi Nitanda
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. CoRR (2019)
Atsushi Nitanda, Taiji Suzuki
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. AISTATS (2019)
Atsushi Nitanda, Taiji Suzuki
Functional Gradient Boosting based on Residual Network Perception. ICML (2018)
Atsushi Nitanda, Taiji Suzuki
Functional Gradient Boosting based on Residual Network Perception. CoRR (2018)
Atsushi Nitanda, Taiji Suzuki
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. CoRR (2018)
Atsushi Nitanda, Taiji Suzuki
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. CoRR (2018)
Atsushi Nitanda, Taiji Suzuki
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. AISTATS (2018)
Atsushi Nitanda, Taiji Suzuki
Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines. AISTATS (2017)
Atsushi Nitanda, Taiji Suzuki
Stochastic Particle Gradient Descent for Infinite Ensembles. CoRR (2017)
Atsushi Nitanda
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. AISTATS (2016)
Atsushi Nitanda
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. CoRR (2015)
Atsushi Nitanda
Stochastic Proximal Gradient Descent with Acceleration Techniques. NIPS (2014)