Monte Carlo Filtering Using Kernel Embedding of Distributions. - Motonobu Kanagawa; Yu Nishiyama; Arthur Gretton; Kenji Fukumizu (AAAI, 2014)

Recovering Distributions from Gaussian RKHS Embeddings. - Motonobu Kanagawa; Kenji Fukumizu (AISTATS, 2014)

Convergence guarantees for kernel-based quadrature rules in misspecified settings. - Motonobu Kanagawa; Bharath K. Sriperumbudur; Kenji Fukumizu (NIPS, 2016)

Filtering with State-Observation Examples via Kernel Monte Carlo Filter. - Motonobu Kanagawa; Yu Nishiyama; Arthur Gretton; Kenji Fukumizu (Neural Comput., 2016)

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)

Kernel Recursive ABC: Point Estimation with Intractable Likelihood. - Takafumi Kajihara; Motonobu Kanagawa; Keisuke Yamazaki; Kenji Fukumizu (ICML, 2018)

Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference. - Krikamol Muandet; Motonobu Kanagawa; Sorawit Saengkyongam; Sanparith Marukatat (CoRR, 2018)

Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. - Motonobu Kanagawa; Philipp Hennig; Dino Sejdinovic; Bharath K. Sriperumbudur (CoRR, 2018)

Convergence Guarantees for Adaptive Bayesian Quadrature Methods. - Motonobu Kanagawa; Philipp Hennig (NeurIPS, 2019)

Model Selection for Simulator-based Statistical Models: A Kernel Approach. - Takafumi Kajihara; Motonobu Kanagawa; Yuuki Nakaguchi; Kanishka Khandelwal; Kenji Fukumizu (CoRR, 2019)

On the positivity and magnitudes of Bayesian quadrature weights. - Toni Karvonen; Motonobu Kanagawa; Simo Särkkä (Stat. Comput., 2019)

Simulator Calibration under Covariate Shift with Kernels. - Keiichi Kisamori; Motonobu Kanagawa; Keisuke Yamazaki (AISTATS, 2020)

Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. - Motonobu Kanagawa; Bharath K. Sriperumbudur; Kenji Fukumizu (Found. Comput. Math., 2020)

Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. - Yu Nishiyama; Motonobu Kanagawa; Arthur Gretton; Kenji Fukumizu (Mach. Learn., 2020)

Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. - Veit Wild; Motonobu Kanagawa; Dino Sejdinovic (CoRR, 2021)

Counterfactual Mean Embeddings. - Krikamol Muandet; Motonobu Kanagawa; Sorawit Saengkyongam; Sanparith Marukatat (J. Mach. Learn. Res., 2021)

When is Importance Weighting Correction Needed for Covariate Shift Adaptation? - Davit Gogolashvili; Matteo Zecchin; Motonobu Kanagawa; Marios Kountouris; Maurizio Filippone (CoRR, 2023)

Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison. - Kensuke Mitsuzawa; Motonobu Kanagawa; Stefano Bortoli; Margherita Grossi; Paolo Papotti (CoRR, 2023)

Fast Computation of Leave-One-Out Cross-Validation for k-NN Regression. - Motonobu Kanagawa (CoRR, 2024)

Variable Selection for Comparing High-dimensional Time-Series Data. - Kensuke Mitsuzawa; Margherita Grossi; Stefano Bortoli; Motonobu Kanagawa (CoRR, 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 (Trans. Mach. Learn. Res., 2024)

Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood. - Masha Naslidnyk; Motonobu Kanagawa; Toni Karvonen; Maren Mahsereci (SIAM/ASA J. Uncertain. Quantification, 2025)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences. - Motonobu Kanagawa; Philipp Hennig; Dino Sejdinovic; Bharath K. Sriperumbudur (CoRR, 2025)