Tomoya Murata

According to our database, Tomoya Murata authored at least 17 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

A Localized Primal-Dual Method for Centralized/Decentralized Federated Learning Robust to Data Heterogeneity.

Iifan Tyou, Tomoya Murata, Takumi Fukami, Yuki Takezawa, Kenta Niwa

IEEE Trans. Signal Inf. Process. over Networks, 2024

DP-Norm: Differential Privacy Primal-Dual Algorithm for Decentralized Federated Learning.

Takumi Fukami, Tomoya Murata, Kenta Niwa, Iifan Tyou

IEEE Trans. Inf. Forensics Secur., 2024

SILVER: Single-loop variance reduction and application to federated learning.

Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity.

Tomoya Murata, Kenta Niwa, Takumi Fukami, Iifan Tyou

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

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

Tomoya Murata, Taiji Suzuki

Proceedings of the International Conference on Machine Learning, 2023

2022

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

Kazusato Oko, Shunta Akiyama, Tomoya Murata, Taiji Suzuki

CoRR, 2022

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

Tomoya Murata, Taiji Suzuki

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Sharp characterization of optimal minibatch size for stochastic finite sum convex optimization.

Atsushi Nitanda, Tomoya Murata, Taiji Suzuki

Knowl. Inf. Syst., 2021

Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning.

Tomoya Murata, Taiji Suzuki

Proceedings of the 38th International Conference on Machine Learning, 2021

Gradient Descent in RKHS with Importance Labeling.

Tomoya Murata, Taiji Suzuki

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error.

Taiji Suzuki, Hiroshi Abe, Tomoya Murata, Shingo Horiuchi, Kotaro Ito, Tokuma Wachi, So Hirai, Masatoshi Yukishima, Tomoaki Nishimura

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019

Accelerated Sparsified SGD with Error Feedback.

Tomoya Murata, Taiji Suzuki

CoRR, 2019

2018

Spectral-Pruning: Compressing deep neural network via spectral analysis.

Taiji Suzuki, Hiroshi Abe, Tomoya Murata, Shingo Horiuchi, Kotaro Ito, Tokuma Wachi, So Hirai, Masatoshi Yukishima, Tomoaki Nishimura

CoRR, 2018

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

Tomoya Murata, Taiji Suzuki

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

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

Tomoya Murata, Taiji Suzuki

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

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

Tomoya Murata, Taiji Suzuki

CoRR, 2016

2014

Hand Gesture and Character Recognition Based on Kinect Sensor.

Tomoya Murata, Jungpil Shin

Int. J. Distributed Sens. Networks, 2014