Publications

Papers presented at conferences and journals in Japan and worldwide, authored by PFN researchers, interns, and part-time engineer students, including results of joint research.

Transactions on Machine Learning Research

A Bayesian Approach to Segmentation with Noisy Labels via Spatially Correlated Distributions

Machine Learning / Deep Learning

By : Ryu Tadokoro, Tsukasa Takagi, Shin-ichi Maeda

NeurIPS2025

Pairwise Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model

Machine Learning / Deep Learning

By : Kotaro Ikeda, Masanori Koyama, Jinzhe Zhang, Kohei Hayashi, Kenji Fukumizu

ICLR2025

Flow matching achieves almost minimax optimal convergence

Machine Learning / Deep Learning

By : Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama

ICML2025

Deep Bayesian Filter for Bayes-Faithful Data Assimilation

Machine Learning / Deep Learning

By : Yuta Tarumi, Keisuke Fukuda, Shin-ichi Maeda

ICML 2023

Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network

Machine Learning / Deep Learning

By : Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda

ICAIF 2023

Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling

Machine Learning / Deep Learning

By : Masanori Hirano, Kentaro Minami, Kentaro Imajo

Publications

A Bayesian Approach to Segmentation with Noisy Labels via Spatially Correlated Distributions

Pairwise Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model

Flow matching achieves almost minimax optimal convergence

Deep Bayesian Filter for Bayes-Faithful Data Assimilation

Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network

Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling

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Join the PFN team

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Contact us for inquiries on our products, solutions, R&D and other case studies