Atsushi Nitanda

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NTT DATA Mathematical Systems Inc.

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The University of Tokyo

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∙ 05/21/2021

Generalization Error Bound for Hyperbolic Ordinal Embedding

Hyperbolic ordinal embedding (HOE) represents entities as points in hype...

Atsushi Suzuki, et al. ∙

∙ 03/11/2021

BODAME: Bilevel Optimization for Defense Against Model Extraction

Model extraction attacks have become serious issues for service provider...

Yuto Mori, et al. ∙

∙ 12/31/2020

Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis

We propose the particle dual averaging (PDA) method, which generalizes t...

Atsushi Nitanda, et al. ∙

∙ 07/31/2020

A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification

Spatial attention has been introduced to convolutional neural networks (...

Linchuan Xu, et al. ∙

∙ 07/23/2020

Online Robust and Adaptive Learning from Data Streams

In online learning from non-stationary data streams, it is both necessar...

Shintaro Fukushima, et al. ∙

∙ 06/22/2020

Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime

We analyze the convergence of the averaged stochastic gradient descent f...

Atsushi Nitanda, et al. ∙

∙ 06/18/2020

When Does Preconditioning Help or Hurt Generalization?

While second order optimizers such as natural gradient descent (NGD) oft...

Shun-ichi Amari, et al. ∙

∙ 11/13/2019

Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features

Although kernel methods are widely used in many learning problems, they ...

Shingo Yashima, et al. ∙

∙ 10/28/2019

Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space

Deep learning has exhibited superior performance for various tasks, espe...

Taiji Suzuki, et al. ∙

∙ 06/20/2019

Data Cleansing for Models Trained with SGD

Data cleansing is a typical approach used to improve the accuracy of mac...

Satoshi Hara, et al. ∙

∙ 05/23/2019

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

Recently, several studies have proven the global convergence and general...

Atsushi Nitanda, et al. ∙

∙ 06/14/2018

Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors

We consider stochastic gradient descent for binary classification proble...

Atsushi Nitanda, et al. ∙

∙ 02/25/2018

Functional Gradient Boosting based on Residual Network Perception

Residual Networks (ResNets) have become state-of-the-art models in deep ...

Atsushi Nitanda, et al. ∙

∙ 01/07/2018

Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models

We propose a new technique that boosts the convergence of training gener...

Atsushi Nitanda, et al. ∙

∙ 12/14/2017

Stochastic Particle Gradient Descent for Infinite Ensembles

The superior performance of ensemble methods with infinite models are we...

Atsushi Nitanda, et al. ∙

∙ 06/09/2015

Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

We propose an optimization method for minimizing the finite sums of smoo...

Atsushi Nitanda, et al. ∙