Motonobu Kanagawa

32 papers A* 3A 2Journal 25Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach. CoRR Parastoo Pashmchi, Jerome Benoit, Motonobu Kanagawa
2025	J	`jnl`	Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood. SIAM/ASA J. Uncertain. Quantification Masha Naslidnyk, Motonobu Kanagawa, Toni Karvonen, Maren Mahsereci
2025	J	`jnl`	Gaussian Processes and Reproducing Kernels: Connections and Equivalences. CoRR Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur
2025	—	`ed.`	Proceedings of the First International Conference on Probabilistic Numerics (ProbNum 2025), 1-3 September 2025, EURECOM, Sophia Antipolis, France ProbNum Motonobu Kanagawa, Jon Cockayne, Alexandra Gessner, Philipp Hennig
2025	J	`jnl`	kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions. CoRR Parastoo Pashmchi, Jerome Benoit, Motonobu Kanagawa
2025	J	`jnl`	kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions. Trans. Mach. Learn. Res. Parastoo Pashmchi, Jerome Benoit, Motonobu Kanagawa
2024	J	`jnl`	Fast Computation of Leave-One-Out Cross-Validation for Trans. Mach. Learn. Res. Motonobu Kanagawa
2024	J	`jnl`	Fast Computation of Leave-One-Out Cross-Validation for k-NN Regression. CoRR Motonobu Kanagawa
2024	J	`jnl`	Improved Random Features for Dot Product Kernels. J. Mach. Learn. Res. Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone
2024	J	`jnl`	Variable Selection for Comparing High-dimensional Time-Series Data. CoRR Kensuke Mitsuzawa, Margherita Grossi, Stefano Bortoli, Motonobu Kanagawa
2023	J	`jnl`	Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison. CoRR Kensuke Mitsuzawa, Motonobu Kanagawa, Stefano Bortoli, Margherita Grossi, Paolo Papotti
2023	J	`jnl`	When is Importance Weighting Correction Needed for Covariate Shift Adaptation? CoRR Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa, Marios Kountouris, Maurizio Filippone
2022	J	`jnl`	Improved Random Features for Dot Product Kernels. CoRR Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone
2021	J	`jnl`	Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. CoRR Veit Wild, Motonobu Kanagawa, Dino Sejdinovic
2021	J	`jnl`	Counterfactual Mean Embeddings. J. Mach. Learn. Res. Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat
2020	J	`jnl`	Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. Found. Comput. Math. Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
2020	J	`jnl`	Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. Mach. Learn. Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu
2020	A	`conf`	Simulator Calibration under Covariate Shift with Kernels. AISTATS Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki
2019	A*	`conf`	Convergence Guarantees for Adaptive Bayesian Quadrature Methods. NeurIPS Motonobu Kanagawa, Philipp Hennig
2019	J	`jnl`	Convergence Guarantees for Adaptive Bayesian Quadrature Methods. CoRR Motonobu Kanagawa, Philipp Hennig
2019	J	`jnl`	Model Selection for Simulator-based Statistical Models: A Kernel Approach. CoRR Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi, Kanishka Khandelwal, Kenji Fukumizu
2019	J	`jnl`	On the positivity and magnitudes of Bayesian quadrature weights. Stat. Comput. Toni Karvonen, Motonobu Kanagawa, Simo Särkkä
2018	J	`jnl`	Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference. CoRR Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat
2018	J	`jnl`	Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. CoRR Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur
2018	A*	`conf`	Kernel Recursive ABC: Point Estimation with Intractable Likelihood. ICML Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu
2018	J	`jnl`	On the positivity and magnitudes of Bayesian quadrature weights. CoRR Toni Karvonen, Motonobu Kanagawa, Simo Särkkä
2017	J	`jnl`	Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. CoRR Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
2017	J	`jnl`	Unsupervised group matching with application to cross-lingual topic matching without alignment information. Data Min. Knowl. Discov. Tomoharu Iwata, Motonobu Kanagawa, Tsutomu Hirao, Kenji Fukumizu
2016	—	`conf`	Convergence guarantees for kernel-based quadrature rules in misspecified settings. NIPS Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
2016	J	`jnl`	Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Comput. Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu
2014	A*	`conf`	Monte Carlo Filtering Using Kernel Embedding of Distributions. AAAI Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu
2014	A	`conf`	Recovering Distributions from Gaussian RKHS Embeddings. AISTATS Motonobu Kanagawa, Kenji Fukumizu