9 papers
Machine-Learning Emulation of Satellite Greenhouse Gas Retrievals: Stability over Time
Nugzar Gognadze, Motonobu Kanagawa, Yu Someya +1
Retrieval algorithms are used to estimate atmospheric concentrations of greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), by solving inverse problems from hi…
Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach
Parastoo Pashmchi, Jérôme Benoit, Motonobu Kanagawa
Missing values are common in photovoltaic (PV) power data, yet the uncertainty they induce is not propagated into predictive distributions. We develop a framework that incorporates…
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…
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…
Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood
Masha Naslidnyk, Motonobu Kanagawa, Toni Karvonen +1
Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted functi…
Gaussian Processes and Reproducing Kernels: 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…