184 citations · 315 across the 23 of their papers we have counts for
16 papers · 1 filter
kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions
Parastoo Pashmchi, Jérôme Benoit, Motonobu Kanagawa
We study a missing-value imputation method, termed kNNSampler, that imputes a given unit's missing response by randomly sampling from the observed responses of the most similar…
Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1
This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using rep…
Fast Computation of Leave-One-Out Cross-Validation for -NN Regression
Motonobu Kanagawa
We describe a fast computation method for leave-one-out cross-validation (LOOCV) for -nearest neighbours (-NN) regression. We show that, under a tie-breaking condition for ne…
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
Kensuke Mitsuzawa, Motonobu Kanagawa, Stefano Bortoli +2
We study two-sample variable selection: identifying variables that discriminate between the distributions of two sets of data vectors. Such variables help scientists understand the…
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa +2
This paper investigates when the importance weighting (IW) correction is needed to address covariate shift, a common situation in supervised learning where the input distributions…
Improved Random Features for Dot Product Kernels
Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone
Dot product kernels, such as polynomial and exponential (softmax) kernels, are among the most widely used kernels in machine learning, as they enable modeling the interactions betw…
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
Veit Wild, Motonobu Kanagawa, Dino Sejdinovic
We investigate the connections between sparse approximation methods for making kernel methods and Gaussian processes (GPs) scalable to large-scale data, focusing on the Nyström met…
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Motonobu Kanagawa, Philipp Hennig
Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical integration that empirically compares favorably with Monte Carlo integration on problems of medium dimensiona…
Model Selection for Simulator-based Statistical Models: A Kernel Approach
Takafumi Kajihara, Motonobu Kanagawa, Yuuki Nakaguchi +2
We propose a novel approach to model selection for simulator-based statistical models. The proposed approach defines a mixture of candidate models, and then iteratively updates the…
Simulator Calibration under Covariate Shift with Kernels
Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki
We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift. Covariate shift is the situation where input distributions for training…
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1
This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or infere…
Counterfactual Mean Embeddings
Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam +1
Counterfactual inference has become a ubiquitous tool in online advertisement, recommendation systems, medical diagnosis, and econometrics. Accurate modeling of outcome distributio…
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki +1
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel…
Convergence guarantees for kernel-based quadrature rules in misspecified settings
Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
Kernel-based quadrature rules are becoming important in machine learning and statistics, as they achieve super- convergence rates in numerical integration, and thus provi…
Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models
Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton +1
Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned fro…
Filtering with State-Observation Examples via Kernel Monte Carlo Filter
Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton +1
This paper addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e. the observation m…