1. KANAGAWA Motonobu
  2. KANAGAWA Motonobu
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KANAGAWA Motonobu

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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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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Journal

Kanagawa, Motonobu

Fast computation of leave-one-out cross-validation for k-NN regression

Transactions on Machine Learning Research, 4 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

Probabilistic Numerics Spring School, 8-10 April 2024, Southampton, UK
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Talk

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

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

UQ 2024, SIAM Conference on Uncertainty Quantification (UQ24), 27 February-1 March 2024, Trieste, Italy
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Journal

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

Variable selection in maximum mean discrepancy for interpretable distribution comparison

Submitted to ArXiV, 2 November 2023
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ARXIV
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Talk

Kanagawa, Motonobu

Variable selection in maximum mean discrepancy for interpretable two-sample testing

Next-Generational Kernel Methods Workshop, 17-19 October 2023, Newcastle, UK
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