Atsushi Nitanda

According to our database1, Atsushi Nitanda authored at least 34 papers between 2014 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction.
CoRR, 2023

Parameter Averaging for SGD Stabilizes the Implicit Bias towards Flat Regions.
CoRR, 2023

Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering.
CoRR, 2023

Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Tight and fast generalization error bound of graph embedding in metric space.
Proceedings of the International Conference on Machine Learning, 2023

Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems.
Proceedings of the International Conference on Machine Learning, 2023

Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Convex Analysis of the Mean Field Langevin Dynamics.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization.
Knowl. Inf. Syst., 2021

BODAME: Bilevel Optimization for Defense Against Model Extraction.
CoRR, 2021

Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generalization Error Bound for Hyperbolic Ordinal Embedding.
Proceedings of the 38th International Conference on Machine Learning, 2021

Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime.
Proceedings of the 9th International Conference on Learning Representations, 2021

When does preconditioning help or hurt generalization?
Proceedings of the 9th International Conference on Learning Representations, 2021

Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis.
CoRR, 2020

A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification.
CoRR, 2020

Online Robust and Adaptive Learning from Data Streams.
CoRR, 2020

Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems.
CoRR, 2019

Data Cleansing for Models Trained with SGD.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Hyperbolic Ordinal Embedding.
Proceedings of The 11th Asian Conference on Machine Learning, 2019

2018
Functional Gradient Boosting based on Residual Network Perception.
Proceedings of the 35th International Conference on Machine Learning, 2018

Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Stochastic Particle Gradient Descent for Infinite Ensembles.
CoRR, 2017

Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2014
Stochastic Proximal Gradient Descent with Acceleration Techniques.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014


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