3 papers
stat.ML2026
Provable Data Scaling Law for Meta Learning via Complexity Minimization
Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui
Pre-training has become a fundamental paradigm in modern machine learning, with one of its key empirical benefits being reduced downstream sample complexity as the scale of pre-tra…
stat.ML2026
Provable Target Sample Complexity Improvements as Pre-Trained Models Scale
Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui
Pre-trained models have become indispensable for efficiently building models across a broad spectrum of downstream tasks. The advantages of pre-trained models have been highlighted…
cs.LG2025
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
Hiroki Naganuma, Ryuichiro Hataya, Kotaro Yoshida +1
In the field of computer vision, fine-tuning pre-trained models has become a prevalent strategy for out-of-distribution (OOD) generalization tasks. Different from most prior work t…