21 citations · 27 across the 4 of their papers we have counts for
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3 papers · 1 filter
stat.ML2014
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…
stat.ML2014★ 5 cited
Characteristic Kernels and Infinitely Divisible Distributions
Yu Nishiyama, Kenji Fukumizu
We connect shift-invariant characteristic kernels to infinitely divisible distributions on . Characteristic kernels play an important role in machine learning appli…
stat.ML2013★ 1 cited
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…