1. KANAGAWA Motonobu
  2. KANAGAWA Motonobu
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KANAGAWA Motonobu

Report

Gognadze, Nugzar; Kanagawa, Motonob; Someya, Yu; Yashiro, Hisashi

Machine-learning emulation of satellite greenhouse gas retrievals: Stability over time

Submitted to ArXiV, 8 June 2026
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ARXIV
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Conference

Kanagawa, Motonobu

Gaussian processes and reproducing kernels: Connections and equivalences

NUMRAD 2026, Numerical Methods for Random Differential Models Summer School, 2-5 June 2026, Lausanne, Switzerland
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ARXIV
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Report

Pashmchi, Parastoo; Benoit, Jérôme; Kanagawa, Motonobu

Predictive uncertainty in short-term PV forecasting under missing data: A multiple imputation approach

Submitted to ArXiV, 16 March 2026
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ARXIV
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Invited paper in a conference

Kanagawa, Motonobu

Gaussian processes and reproducing kernels: Connections and equivalences

FIMI 2026, Workshop on Functional Inference and Machine Intelligence, 2-5 March 2026, Tokyo, Japan
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Journal

Pashmchi, Parastoo; Benoit, Jéröme; Kanagawa, Motonobu

kNNSampler: Stochastic imputations for recovering missing value distributions

Transactions on Machine Learning Research, December 2025
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Document
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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ARXIV
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HAL
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