1. KANAGAWA Motonobu
  2. KANAGAWA Motonobu
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KANAGAWA Motonobu

Report

Pashmchi, Parastoo; Benoit, Jéröme; Kanagawa, Motonobu

kNNSampler: Stochastic imputations for recovering missing value distributions

Submitted to ArXiV, 10 September 2025
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ARXIV
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Conference

Kanagawa, Motonobu; Cockayne, Jon; Gessner, Alexandra; Hennig, Philipp

Editor of the 1st International Conference on Probabilistic Numerics

PROBNUM 2025, 1st International Conference on Probabilistic Numerics, 1-3 September 2025, Sophia Antipolis, France / Proceedings of Machine Learning Research (PMLR), Vol. 271, September 2025
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Talk

Kanagawa, Motonobu

Well-calibrated uncertainty quantification with Gaussian processes

Invited talk at France-Japan Joint Workshop 2025, 3-4 July 2025, Sophia Antipolis, France

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Report

Kanagawa, Motonobu; Hennig, Philipp; Sejdinovic, Dino; Sriperumbudur, Bharath K.

Gaussian Processes and Reproducing Kernels: Connections and Equivalences

Submitted to ArXiV, 20 June 2025
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ARXIV
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Journal

Naslidnyk, Masha; Kanagawa, Motonobu; Karvonen, Toni; Mahsereci, Maren

Comparing scale parameter estimators for Gaussian process interpolation with the brownian motion prior: Leave-one-out cross validation and maximum likelihood

SIAM/ASA Journal on Uncertainty Quantification, Vol. 13, N°2, May 2025
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ARXIV
DOI
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Journal

Wacker, Jonas; Kanagawa, Motonobu; Filippone, Maurizio

Improved random features for dot product Kernels

The Journal of Machine Learning Research (JMLR), Vol. 25, N°1, Article N°235, 18 March 2025
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Document
ARXIV
DOI
HAL
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Talk

Motonobu, Kanagawa

Comparing scale parameter estimators for Gaussian process regression: Cross validation and maximum likelihood

FIMI 2025, Workshop on Functional Inference and Machine Intelligence, 1-2 March 2025, Okinawa, Japan
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Journal

Mitsuzawa, Kensuke; Grossi, Margherita; Bortoli, Stefano; Kanagawa, Motonobu

Variable selection for comparing high-dimensional time-series data

Submitted to ArXiV, 9 December 2024
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ARXIV
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Talk

Kanagawa, Motonobu

Comparing scale parameter estimators for Gaussian process regression: Cross validation and maximum likelihood

62nd Statistical Machine Learning Seminar, 14 November 2024, Tokyo, Japan
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Poster / Demo

Talebinamvar, Mobina; Kanagawa, Motonobu; Forget, Didier; Sauval, Côme

Storm surge simulation for inundation risk assessment

Data Science Scientific Council at EURECOM, 31 May 2024, Sophia Antipolis, France
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