Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation. - Kazuki Osawa; Akira Sekiya; Hiroki Naganuma; Rio Yokota (HPCS, 2017)

A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training. - Hiroki Naganuma; Rio Yokota (CCGRID, 2019)

Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation. - Kilian Fatras; Hiroki Naganuma; Ioannis Mitliagkas (CoLLAs, 2022)

Conjugate Gradient Method for Generative Adversarial Networks. - Hiroki Naganuma; Hideaki Iiduka (AISTATS, 2023)

How Image Corruption and Perturbation Affect Out-of-Distribution Generalization and Calibration. - Keigo Tada; Hiroki Naganuma (IJCNN, 2023)

Story-to-Images Translation: Leveraging Diffusion Models and Large Language Models for Sequence Image Generation. - Haruka Kumagai; Ryosuke Yamaki; Hiroki Naganuma (NarSUM@MM, 2023)

An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration. - Hiroki Naganuma; Ryuichiro Hataya (CoRR, 2023)

Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. - Hiroki Naganuma; Kartik Ahuja; Shiro Takagi; Tetsuya Motokawa; Rio Yokota; Kohta Ishikawa; Ikuro Sato; Ioannis Mitliagkas (Trans. Mach. Learn. Res., 2023)

No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths. - Charles Guille-Escuret; Hiroki Naganuma; Kilian Fatras; Ioannis Mitliagkas (ICML, 2024)

Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation. - Yuji Naraki; Ryosuke Yamaki; Yoshikazu Ikeda; Takafumi Horie; Hiroki Naganuma (CoRR, 2024)

Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration. - Kotaro Yoshida; Hiroki Naganuma (Trans. Mach. Learn. Res., 2024)

Mastering Task Arithmetic: τJp as a Key Indicator for Weight Disentanglement. - Kotaro Yoshida; Yuji Naraki; Takafumi Horie; Ryosuke Yamaki; Ryotaro Shimizu; Yuki Saito 002; Julian J. McAuley; Hiroki Naganuma (ICLR, 2025)

Geometric insights into focal loss: Reducing curvature for enhanced model calibration. - Masanari Kimura; Hiroki Naganuma (Pattern Recognit. Lett., 2025)

An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration. - Hiroki Naganuma; Ryuichiro Hataya; Kotaro Yoshida; Ioannis Mitliagkas (Trans. Mach. Learn. Res., 2025)

Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training. - Hiroki Naganuma; Xinzhi Zhang 002; Man-Chung Yue; Ioannis Mitliagkas; Philipp A. Witte; Russell J. Hewett; Yin Tat Lee (CoRR, 2025)

On Fairness of Task Arithmetic: The Role of Task Vectors. - Hiroki Naganuma; Kotaro Yoshida; Laura Gomezjurado Gonzalez; Takafumi Horie; Yuji Naraki; Ryotaro Shimizu (CoRR, 2025)

Analysis of Muon's Convergence and Critical Batch Size. - Naoki Sato; Hiroki Naganuma; Hideaki Iiduka (CoRR, 2025)

DisTaC: Conditioning Task Vectors via Distillation for Robust Model Merging. - Kotaro Yoshida; Yuji Naraki; Takafumi Horie; Ryotaro Shimizu; Hiroki Naganuma (CoRR, 2025)