Tomoya Murata | DeepAI

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∙ 02/08/2023

DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning

Differential private optimization for nonconvex smooth objective is cons...

0 Tomoya Murata, et al. ∙

∙ 09/01/2022

Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning

While variance reduction methods have shown great success in solving lar...

0 Kazusato Oko, et al. ∙

∙ 02/12/2022

Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning

In recent centralized nonconvex distributed learning and federated learn...

0 Tomoya Murata, et al. ∙

∙ 02/05/2021

Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning

Federated learning is one of the important learning scenarios in distrib...

0 Tomoya Murata, et al. ∙

∙ 06/19/2020

Gradient Descent in RKHS with Importance Labeling

Labeling cost is often expensive and is a fundamental limitation of supe...

15 Tomoya Murata, et al. ∙

∙ 05/29/2019

Accelerated Sparsified SGD with Error Feedback

We study a stochastic gradient method for synchronous distributed optimi...

0 Tomoya Murata, et al. ∙

∙ 09/05/2018

Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation

We develop new stochastic gradient methods for efficiently solving spars...

0 Tomoya Murata, et al. ∙

∙ 08/26/2018

Spectral-Pruning: Compressing deep neural network via spectral analysis

The model size of deep neural network is getting larger and larger to re...

0 Taiji Suzuki, et al. ∙

∙ 03/01/2017

Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization

In this paper, we develop a new accelerated stochastic gradient method f...

0 Tomoya Murata, et al. ∙

∙ 03/08/2016

Stochastic dual averaging methods using variance reduction techniques for regularized empirical risk minimization problems

We consider a composite convex minimization problem associated with regu...

0 Tomoya Murata, et al. ∙