Motonobu Kanagawa

According to our database, Motonobu Kanagawa authored at least 20 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Improved Random Features for Dot Product Kernels.

Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone

J. Mach. Learn. Res., 2024

Fast Computation of Leave-One-Out Cross-Validation for k-NN Regression.

Motonobu Kanagawa

CoRR, 2024

2023

Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison.

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

CoRR, 2023

When is Importance Weighting Correction Needed for Covariate Shift Adaptation?

Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa, Marios Kountouris, Maurizio Filippone

CoRR, 2023

2021

Counterfactual Mean Embeddings.

Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat

J. Mach. Learn. Res., 2021

Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes.

Veit Wild, Motonobu Kanagawa, Dino Sejdinovic

CoRR, 2021

2020

Model-based kernel sum rule: kernel Bayesian inference with probabilistic models.

Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu

Mach. Learn., 2020

Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings.

Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu

Found. Comput. Math., 2020

Simulator Calibration under Covariate Shift with Kernels.

Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

On the positivity and magnitudes of Bayesian quadrature weights.

Toni Karvonen, Motonobu Kanagawa, Simo Särkkä

Stat. Comput., 2019

Model Selection for Simulator-based Statistical Models: A Kernel Approach.

Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi, Kanishka Khandelwal, Kenji Fukumizu

CoRR, 2019

Convergence Guarantees for Adaptive Bayesian Quadrature Methods.

Motonobu Kanagawa, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences.

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

CoRR, 2018

Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference.

Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat

CoRR, 2018

Kernel Recursive ABC: Point Estimation with Intractable Likelihood.

Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Unsupervised group matching with application to cross-lingual topic matching without alignment information.

Tomoharu Iwata, Motonobu Kanagawa, Tsutomu Hirao, Kenji Fukumizu

Data Min. Knowl. Discov., 2017

2016

Filtering with State-Observation Examples via Kernel Monte Carlo Filter.

Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu

Neural Comput., 2016

Convergence guarantees for kernel-based quadrature rules in misspecified settings.

Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2014

Recovering Distributions from Gaussian RKHS Embeddings.

Motonobu Kanagawa, Kenji Fukumizu

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Monte Carlo Filtering Using Kernel Embedding of Distributions.

Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014