12 citations · 44 across the 17 of their papers we have counts for
17 papers
Overcoming Model Misspecification in Bayesian Inference of Molecular Signalling Networks
Yinghan Li, Heishiro Kanagawa, Francesca Romana Crucinio +2
Bayesian inference of molecular signalling networks usually relies on tractability of the marginal likelihood, enabling the set of possible networks to be efficiently explored. As…
Thinned Mean Field Langevin Dynamics
Zonghao Chen, Heishiro Kanagawa, François-Xavier Briol +2
Several important learning tasks can be formulated as minimizing an entropy-regularized objective over an appropriate space of probability distributions. Mean-field Langevin dynami…
A Computable Measure of Suboptimality for Entropy-Regularised Variational Objectives
Clémentine Chazal, Heishiro Kanagawa, Zheyang Shen +2
Several methods in statistics and machine learning target a probability distribution for which an entropy-regularised variational objective is minimised. This increased flexibility…
Harnessing the Power of Reinforcement Learning for Adaptive MCMC
Congye Wang, Matthew A. Fisher, Heishiro Kanagawa +2
Sampling algorithms drive probabilistic machine learning, and recent years have seen an explosion in the diversity of tools for this task. However, the increasing sophistication of…
Stationary MMD Points
Zonghao Chen, Toni Karvonen, Heishiro Kanagawa +2
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical integration. Several authors have proposed to se…
Fast Approximate Solution of Stein Equations for Post-Processing of MCMC
Qingyang Liu, Heishiro Kanagawa, Matthew A. Fisher +2
Bayesian inference is conceptually elegant, but calculating posterior expectations can entail a heavy computational cost. Monte Carlo methods are reliable and supported by strong a…
Reinforcement Learning for Adaptive MCMC
Congye Wang, Wilson Chen, Heishiro Kanagawa +1
An informal observation, made by several authors, is that the adaptive design of a Markov transition kernel has the flavour of a reinforcement learning task. Yet, to-date it has re…
Stein -Importance Sampling
Congye Wang, Wilson Chen, Heishiro Kanagawa +1
Stein discrepancies have emerged as a powerful tool for retrospective improvement of Markov chain Monte Carlo output. However, the question of how to design Markov chains that are…
Controlling Moments with Kernel Stein Discrepancies
Heishiro Kanagawa, Alessandro Barp, Arthur Gretton +1
Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. No…
A kernel Stein test of goodness of fit for sequential models
Jerome Baum, Heishiro Kanagawa, Arthur Gretton
We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length seq…
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
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 informa…
Blindness of score-based methods to isolated components and mixing proportions
Li K. Wenliang, Heishiro Kanagawa
Statistical tasks such as density estimation and approximate Bayesian inference often involve densities with unknown normalising constants. Score-based methods, including score mat…
Testing Goodness of Fit of Conditional Density Models with Kernels
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 and a joint sample, decid…
Amortised Learning by Wake-Sleep
Li K. Wenliang, Theodore Moskovitz, Heishiro Kanagawa +1
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 f…
A Kernel Stein Test for Comparing Latent Variable Models
Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey +2
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…
Informative Features for Model Comparison
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy +3
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. We propose two new statistical tes…
Cross-domain Recommendation via Deep Domain Adaptation
Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu +2
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 use…