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ORCIDAtsushi Nitanda
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2020 – today
- 2024
[c28]Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu:
Why is parameter averaging beneficial in SGD? An objective smoothing perspective. AISTATS 2024: 3565-3573- [c27]Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki:
Koopman-based generalization bound: New aspect for full-rank weights. ICLR 2024 - [c26]Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu:
Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data. ICLR 2024 - [i24]Atsushi Nitanda:
Improved Particle Approximation Error for Mean Field Neural Networks. CoRR abs/2405.15767 (2024) - [i23]Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong:
Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. CoRR abs/2411.02199 (2024) - 2023
- [c25]Taiji Suzuki, Atsushi Nitanda, Denny Wu:
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. ICLR 2023 - [c24]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 - [c23]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: 33268-33284 - [c22]Taiji Suzuki, Denny Wu, Atsushi Nitanda:
Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction. NeurIPS 2023 - [c21]Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda:
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond. NeurIPS 2023 - [i22]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 abs/2302.05825 (2023) - [i21]Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda:
Parameter Averaging for SGD Stabilizes the Implicit Bias towards Flat Regions. CoRR abs/2302.09376 (2023) - [i20]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) - [i19]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 abs/2305.07971 (2023) - [i18]Taiji Suzuki, Denny Wu, Atsushi Nitanda:
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction. CoRR abs/2306.07221 (2023) - 2022
- [c20]Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Convex Analysis of the Mean Field Langevin Dynamics. AISTATS 2022: 9741-9757 - [c19]Kazusato Oko, Taiji Suzuki, Atsushi Nitanda, Denny Wu:
Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization. ICLR 2022 - [c18]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 - [i17]Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Convex Analysis of the Mean Field Langevin Dynamics. CoRR abs/2201.10469 (2022) - 2021
- [j1]Atsushi Nitanda, Tomoya Murata, Taiji Suzuki:
Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization. Knowl. Inf. Syst. 63(9): 2513-2539 (2021) - [c17]Shingo Yashima, Atsushi Nitanda, Taiji Suzuki:
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. AISTATS 2021: 1954-1962 - [c16]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 - [c15]Atsushi Nitanda, Taiji Suzuki:
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. ICLR 2021 - [c14]Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza:
Generalization Error Bound for Hyperbolic Ordinal Embedding. ICML 2021: 10011-10021 - [c13]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: 1243-1255 - [c12]Taiji Suzuki, Atsushi Nitanda:
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. NeurIPS 2021: 3609-3621 - [c11]Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis. NeurIPS 2021: 19608-19621 - [i16]Yuto Mori, Atsushi Nitanda, Akiko Takeda:
BODAME: Bilevel Optimization for Defense Against Model Extraction. CoRR abs/2103.06797 (2021) - [i15]Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Marc Cavazza, Kenji Yamanishi:
Generalization Error Bound for Hyperbolic Ordinal Embedding. CoRR abs/2105.10475 (2021) - 2020
- [c10]Atsushi Nitanda, Taiji Suzuki:
Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. AISTATS 2020: 2981-2991 - [i14]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 abs/2006.10732 (2020) - [i13]Atsushi Nitanda, Taiji Suzuki:
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. CoRR abs/2006.12297 (2020) - [i12]Shintaro Fukushima, Atsushi Nitanda, Kenji Yamanishi:
Online Robust and Adaptive Learning from Data Streams. CoRR abs/2007.12160 (2020) - [i11]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 abs/2007.15897 (2020) - [i10]Atsushi Nitanda, Denny Wu, Taiji Suzuki:
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis. CoRR abs/2012.15477 (2020)
2010 – 2019
- 2019
- [c9]Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi:
Hyperbolic Ordinal Embedding. ACML 2019: 1065-1080 - [c8]Atsushi Nitanda, Taiji Suzuki:
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. AISTATS 2019: 1417-1426 - [c7]Atsushi Nitanda, Tomoya Murata, Taiji Suzuki:
Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization. ICDM 2019: 488-497 - [c6]Satoshi Hara, Atsushi Nitanda, Takanori Maehara:
Data Cleansing for Models Trained with SGD. NeurIPS 2019: 4215-4224 - [i9]Atsushi Nitanda, Taiji Suzuki:
Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems. CoRR abs/1905.09870 (2019) - [i8]Satoshi Hara, Atsushi Nitanda, Takanori Maehara:
Data Cleansing for Models Trained with SGD. CoRR abs/1906.08473 (2019) - [i7]Taiji Suzuki, Atsushi Nitanda:
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. CoRR abs/1910.12799 (2019) - [i6]Shingo Yashima, Atsushi Nitanda, Taiji Suzuki:
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. CoRR abs/1911.05350 (2019) - 2018
- [c5]Atsushi Nitanda, Taiji Suzuki:
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. AISTATS 2018: 1008-1016 - [c4]Atsushi Nitanda, Taiji Suzuki:
Functional Gradient Boosting based on Residual Network Perception. ICML 2018: 3816-3825 - [i5]Atsushi Nitanda, Taiji Suzuki:
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. CoRR abs/1801.02227 (2018) - [i4]Atsushi Nitanda, Taiji Suzuki:
Functional Gradient Boosting based on Residual Network Perception. CoRR abs/1802.09031 (2018) - [i3]Atsushi Nitanda, Taiji Suzuki:
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. CoRR abs/1806.05438 (2018) - 2017
- [c3]Atsushi Nitanda, Taiji Suzuki:
Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines. AISTATS 2017: 470-478 - [i2]Atsushi Nitanda, Taiji Suzuki:
Stochastic Particle Gradient Descent for Infinite Ensembles. CoRR abs/1712.05438 (2017) - 2016
- [c2]Atsushi Nitanda:
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. AISTATS 2016: 195-203 - 2015
- [i1]Atsushi Nitanda:
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums. CoRR abs/1506.03016 (2015) - 2014
- [c1]Atsushi Nitanda:
Stochastic Proximal Gradient Descent with Acceleration Techniques. NIPS 2014: 1574-1582
Coauthor Index
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