論文・研究業績
論文ごとの書誌情報、識別子、関連資料、引用情報を掲載しています。
ジャーナル論文
Generalization Measures under Controlled Covariate Shift: A Regime-Aware Benchmark
Transactions on Machine Learning Research, 2026年8月（採択済み）
What do near-optimal learning rate schedules look like?
Transactions on Machine Learning Research, 2026年6月
Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training
Transactions on Machine Learning Research, 2025年8月
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
Transactions on Machine Learning Research, 2025年4月
Geometric Insights into Focal Loss: Reducing Curvature for Enhanced Model Calibration
Pattern Recognition Letters, 2025年2月
Towards Understanding Variants of Invariant Risk Minimization from the Perspective of Calibration
Transactions on Machine Learning Research, 2024年6月
Empirical Study on Optimizer Selection for Out-of-Distribution Generalizations
Transactions on Machine Learning Research, 2023年6月
国際会議論文
Adaptive Batch Sizes Using Non-Euclidean Gradient Noise Scales for Stochastic Sign and Spectral Descent
International Conference on Machine Learning, 2026年7月
On Fairness of Task Arithmetic: The Role of Task Vectors
International Conference on Learning Representations, 2026年1月
DiTaC: Conditioning Task Vectors via Distillation for Robust Model Merging
International Conference on Learning Representations, 2026年1月
Mastering Task Arithmetic: τJp as a Key Indicator for Weight Disentanglement
International Conference on Learning Representations, 2025年4月
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
International Conference on Machine Learning, 2024年7月
How Image Corruption and Perturbation Affect Out-Of-Distribution Generalization and Calibration
International Joint Conference on Neural Networks, 2023年6月
Conjugate Gradient Method for Generative Adversarial Networks
International Conference on Artificial Intelligence and Statistics, 2023年5月
Optimal Transport Meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation
Conference on Lifelong Learning Agents, 2022年8月
Accelerating Convolutional Neural Networks Using Low Precision Arithmetic
International Conference on High Performance Computing in Asia-Pacific Region, 2018年1月
Accelerating Matrix Multiplication in Deep Learning by using Low-Rank Approximation
International Conference on High Performance Computing & Simulation, 2017年7月
プレプリント・査読中
Which Geometry on Which Layer? A Principled Criterion for Mixed-Optimizer Training
Under Review, 2026年5月（査読中）
Orth-Dion: Eliminating Geometric Mismatch in Distributed Low-Rank Spectral Optimization
Under Review, 2026年5月（査読中）
Convergence Bound and Critical Batch Size of Muon Optimizer
Under Review, 2025年7月（査読中）
When Does Alignment Help? A Comparative Study of DCCA and Fusion-Based Approaches for Multi-modal Chest X-ray Analysis
SSRN, 2025年4月
Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation
arXiv, 2024年3月
Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime
arXiv, 2021年9月
ワークショップ論文
The Geometry of Spectral Gradient Descent: Layerwise Criteria for SignSGD vs SpecSGD
ICLR 2026 Workshop on Geometry-grounded Representation Learning and Generative Modeling, 2026年2月
Smoothness-Adaptive Sharpness Aware Minimization for Finding Flatter Minima
ICLR 2024 Workshop on Practical ML for Low Resource Settings, 2024年5月
Story-to-Images Translation: Leveraging Diffusion Models and Large Language Models for Sequence Image Generation
ACM Multimedia 2023 Workshop on User-centric Narrative Summarization of Long Videos, 2023年10月
On the Interplay of Curvature, Calibration and Out-of-Distribution Generalization: Insights from SAM and Focal Loss Analyses
ICCV 2023 Workshop on Uncertainty Quantification for Computer Vision, 2023年10月
Necessary and Sufficient Hypothesis of Curvature: Understanding Connection Between Out-of-Distribution Generalization and Calibration
ICLR 2023 Workshop on Domain Generalization, 2023年5月
Towards Understanding the Relationship of Batch Size and Iterations in Deep Learning
Machine Learning Summer School 2020, 2020年6月
On Empirical Analysis of Layer-wise Learning Rate Schedule
Asian Conference on Machine Learning 2019 Workshop, 2019年11月
A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training
2nd High Performance Machine Learning Workshop, 2019年5月
Noise Injection Leads to Better Generalization in Large Mini-Batch Training
Tokyo Institute of Technology and Stony Brook University Joint Science and Technology Meeting, 2019年5月
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