Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Kanagawa, Motonobu"'
Autor:
Kanagawa, Motonobu
We describe a fast computation method for leave-one-out cross-validation (LOOCV) for $k$-nearest neighbours ($k$-NN) regression. We show that, under a tie-breaking condition for nearest neighbours, the LOOCV estimate of the mean square error for $k$-
Externí odkaz:
http://arxiv.org/abs/2405.04919
Two-sample testing decides whether two datasets are generated from the same distribution. This paper studies variable selection for two-sample testing, the task being to identify the variables (or dimensions) responsible for the discrepancies between
Externí odkaz:
http://arxiv.org/abs/2311.01537
Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted function values. For the quantified uncertainties to be well-calibrated, how
Externí odkaz:
http://arxiv.org/abs/2307.07466
Autor:
Gogolashvili, Davit, Zecchin, Matteo, Kanagawa, Motonobu, Kountouris, Marios, Filippone, Maurizio
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 of training and test data differ. Classic results show that the IW cor
Externí odkaz:
http://arxiv.org/abs/2303.04020
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 between input features, which is crucial in applications like computer vis
Externí odkaz:
http://arxiv.org/abs/2201.08712
We study a fully funded, collective defined-contribution (DC) pension system with multiple overlapping generations. We investigate whether the welfare of participants can be improved by intergenerational risk sharing (IRS) implemented with a realisti
Externí odkaz:
http://arxiv.org/abs/2106.13644
We investigate the connections between sparse approximation methods for making kernel methods and Gaussian processes (GPs) scalable to large-scale data, focusing on the Nystr\"om method and the Sparse Variational Gaussian Processes (SVGP). While spar
Externí odkaz:
http://arxiv.org/abs/2106.01121
Autor:
Kanagawa, Motonobu, Hennig, Philipp
Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical integration that empirically compares favorably with Monte Carlo integration on problems of medium dimensionality (where non-adaptive quadrature is not competitive). Its key ingre
Externí odkaz:
http://arxiv.org/abs/1905.10271
Autor:
Kajihara, Takafumi, Kanagawa, Motonobu, Nakaguchi, Yuuki, Khandelwal, Kanishka, Fukumiziu, Kenji
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 weight coefficients for those models as well as the parameters in eac
Externí odkaz:
http://arxiv.org/abs/1902.02517
This article reviews and studies the properties of Bayesian quadrature weights, which strongly affect stability and robustness of the quadrature rule. Specifically, we investigate conditions that are needed to guarantee that the weights are positive
Externí odkaz:
http://arxiv.org/abs/1812.08509