Motonobu Kanagawa | DeepAI

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∙ 07/14/2023

Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood

Gaussian process (GP) regression is a Bayesian nonparametric method for ...

0 Masha Naslidnyk, et al. ∙

∙ 03/07/2023

When is Importance Weighting Correction Needed for Covariate Shift Adaptation?

This paper investigates when the importance weighting (IW) correction is...

0 Davit Gogolashvili, et al. ∙

∙ 01/21/2022

Improved Random Features for Dot Product Kernels

Dot product kernels, such as polynomial and exponential (softmax) kernel...

0 Jonas Wacker, et al. ∙

∙ 06/02/2021

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

We investigate the connections between sparse approximation methods for ...

0 Veit Wild, et al. ∙

∙ 05/24/2019

Convergence Guarantees for Adaptive Bayesian Quadrature Methods

Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical i...

0 Motonobu Kanagawa, et al. ∙

∙ 02/07/2019

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

We propose a novel approach to model selection for simulator-based stati...

0 Takafumi Kajihara, et al. ∙

∙ 12/20/2018

On the positivity and magnitudes of Bayesian quadrature weights

This article reviews and studies the properties of Bayesian quadrature w...

0 Toni Karvonen, et al. ∙

∙ 07/06/2018

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

This paper is an attempt to bridge the conceptual gaps between researche...

0 Motonobu Kanagawa, et al. ∙

∙ 05/22/2018

Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

This paper introduces a novel Hilbert space representation of a counterf...

0 Krikamol Muandet, et al. ∙

∙ 02/23/2018

Kernel Recursive ABC: Point Estimation with Intractable Likelihood

We propose a novel approach to parameter estimation for simulator-based ...

0 Takafumi Kajihara, et al. ∙

∙ 09/01/2017

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

This paper presents convergence analysis of kernel-based quadrature rule...

0 Motonobu Kanagawa, et al. ∙

∙ 05/24/2016

Convergence guarantees for kernel-based quadrature rules in misspecified settings

Kernel-based quadrature rules are becoming important in machine learning...

0 Motonobu Kanagawa, et al. ∙

∙ 12/17/2013

Filtering with State-Observation Examples via Kernel Monte Carlo Filter

This paper addresses the problem of filtering with a state-space model. ...

0 Motonobu Kanagawa, et al. ∙

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