Search Results for author: Heishiro Kanagawa

Found 8 papers, 2 papers with code

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation

no code implementations NeurIPS 2021 Liyuan Xu, Heishiro Kanagawa, Arthur Gretton

Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder.

Blindness of score-based methods to isolated components and mixing proportions

no code implementations23 Aug 2020 Li K. Wenliang, Heishiro Kanagawa

Statistical tasks such as density estimation and approximate Bayesian inference often involve densities with unknown normalising constants.

Bayesian Inference Density Estimation +1

Testing Goodness of Fit of Conditional Density Models with Kernels

1 code implementation24 Feb 2020 Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf

We propose two nonparametric statistical tests of goodness of fit for conditional distributions: given a conditional probability density function $p(y|x)$ and a joint sample, decide whether the sample is drawn from $p(y|x)r_x(x)$ for some density $r_x$.

Two-sample testing

Amortised Learning by Wake-Sleep

no code implementations ICML 2020 Li K. Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani

Models that employ latent variables to capture structure in observed data lie at the heart of many current unsupervised learning algorithms, but exact maximum-likelihood learning for powerful and flexible latent-variable models is almost always intractable.

A Kernel Stein Test for Comparing Latent Variable Models

no code implementations1 Jul 2019 Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton

We propose a kernel-based nonparametric test of relative goodness of fit, where the goal is to compare two models, both of which may have unobserved latent variables, such that the marginal distribution of the observed variables is intractable.

Informative Features for Model Comparison

3 code implementations NeurIPS 2018 Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton

Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models.

Cross-domain Recommendation via Deep Domain Adaptation

no code implementations8 Mar 2018 Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, Taiji Suzuki

The behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never used.

Collaborative Filtering Denoising +2

Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning

no code implementations NeurIPS 2016 Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami

We investigate the statistical performance and computational efficiency of the alternating minimization procedure for nonparametric tensor learning.

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