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ORCIDMotonobu Kanagawa
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2020 – today
- 2024
[j7]Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone:
Improved Random Features for Dot Product Kernels. J. Mach. Learn. Res. 25: 235:1-235:75 (2024)- [i11]Motonobu Kanagawa:
Fast Computation of Leave-One-Out Cross-Validation for k-NN Regression. CoRR abs/2405.04919 (2024) - 2023
- [i10]Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa, Marios Kountouris, Maurizio Filippone:
When is Importance Weighting Correction Needed for Covariate Shift Adaptation? CoRR abs/2303.04020 (2023) - [i9]Kensuke Mitsuzawa, Motonobu Kanagawa, Stefano Bortoli, Margherita Grossi, Paolo Papotti:
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison. CoRR abs/2311.01537 (2023) - 2022
- [i8]Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone:
Improved Random Features for Dot Product Kernels. CoRR abs/2201.08712 (2022) - 2021
- [j6]Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat:
Counterfactual Mean Embeddings. J. Mach. Learn. Res. 22: 162:1-162:71 (2021) - [i7]Veit Wild, Motonobu Kanagawa, Dino Sejdinovic:
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. CoRR abs/2106.01121 (2021) - 2020
- [j5]Motonobu Kanagawa
, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. Found. Comput. Math. 20(1): 155-194 (2020) - [j4]Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu:
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. Mach. Learn. 109(5): 939-972 (2020) - [c6]Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki:
Simulator Calibration under Covariate Shift with Kernels. AISTATS 2020: 1244-1253
2010 – 2019
- 2019
- [j3]Toni Karvonen, Motonobu Kanagawa, Simo Särkkä:
On the positivity and magnitudes of Bayesian quadrature weights. Stat. Comput. 29(6): 1317-1333 (2019) - [c5]Motonobu Kanagawa, Philipp Hennig:
Convergence Guarantees for Adaptive Bayesian Quadrature Methods. NeurIPS 2019: 6234-6245 - [i6]Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi, Kanishka Khandelwal, Kenji Fukumizu:
Model Selection for Simulator-based Statistical Models: A Kernel Approach. CoRR abs/1902.02517 (2019) - [i5]Motonobu Kanagawa, Philipp Hennig:
Convergence Guarantees for Adaptive Bayesian Quadrature Methods. CoRR abs/1905.10271 (2019) - 2018
- [c4]Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu:
Kernel Recursive ABC: Point Estimation with Intractable Likelihood. ICML 2018: 2405-2414 - [i4]Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, Sanparith Marukatat:
Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference. CoRR abs/1805.08845 (2018) - [i3]Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur:
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. CoRR abs/1807.02582 (2018) - [i2]Toni Karvonen, Motonobu Kanagawa, Simo Särkkä:
On the positivity and magnitudes of Bayesian quadrature weights. CoRR abs/1812.08509 (2018) - 2017
- [j2]Tomoharu Iwata, Motonobu Kanagawa, Tsutomu Hirao, Kenji Fukumizu:
Unsupervised group matching with application to cross-lingual topic matching without alignment information. Data Min. Knowl. Discov. 31(2): 350-370 (2017) - [i1]Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. CoRR abs/1709.00147 (2017) - 2016
- [j1]Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu:
Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Comput. 28(2): 382-444 (2016) - [c3]Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu:
Convergence guarantees for kernel-based quadrature rules in misspecified settings. NIPS 2016: 3288-3296 - 2014
- [c2]Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu:
Monte Carlo Filtering Using Kernel Embedding of Distributions. AAAI 2014: 1897-1903 - [c1]Motonobu Kanagawa, Kenji Fukumizu:
Recovering Distributions from Gaussian RKHS Embeddings. AISTATS 2014: 457-465
Coauthor Index
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