Futoshi Futami (Ph.D.)
Title
Team Director
Members
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Visiting scientistNontawat Charoenphakdee
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Visiting scientistKeisuke Hanada
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Visiting scientistMatthew Holland
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Part-time worker IINaoto Tani
Introduction
As machine learning is increasingly applied in high-stakes domains, it is critical not only to ensure accuracy but also to quantify predictive uncertainty. Our team develops theoretical frameworks and algorithms to evaluate and control uncertainty, using tools from statistical learning theory, information theory, and Bayesian statistics. We focus on calibration of predicted probabilities, epistemic uncertainty, and latent variable models. By deepening the mathematical foundations of these topics, we aim to advance the development of reliable machine learning systems with rigorous uncertainty quantification.
Main Research Field
Informatics
Research Field
Engineering / Mathematical & Physical Sciences / Intelligent informatics-related / Theory of informatics-related / Theory of informatics-related
Research Subjects
Machine learning
Bayesian inference
Uncertainty evaluation
Bayesian inference
Uncertainty evaluation
RIKEN Website URL
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