| 2025 | AISTATS | Clustered Invariant Risk Minimization. | Tomoya Murata, Atsushi Nitanda, Taiji Suzuki |
| 2024 | ICLR | Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity. | Tomoya Murata, Kenta Niwa, Takumi Fukami, Iifan Tyou |
| 2024 | ICML | SILVER: Single-loop variance reduction and application to federated learning. | Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki |
| 2023 | ICML | DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning. | Tomoya Murata, Taiji Suzuki |
| 2021 | AISTATS | Gradient Descent in RKHS with Importance Labeling. | Tomoya Murata, Taiji Suzuki |
| 2021 | ICML | Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. | Tomoya Murata, Taiji Suzuki |
| 2020 | IJCAI | 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 |
| 2019 | ICDM | Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization. | Atsushi Nitanda, Tomoya Murata, Taiji Suzuki |