
Learning deep kernels for exponential family densities
The kernel exponential family is a rich class of distributions,which can...
11/20/2018 ∙ by Li Wenliang, et al. ∙ 46 ∙ shareread it

Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
04/27/2019 ∙ by Bo Dai, et al. ∙ 28 ∙ shareread it

Kernel Instrumental Variable Regression
Instrumental variable regression is a strategy for learning causal relat...
06/01/2019 ∙ by Rahul Singh, et al. ∙ 14 ∙ shareread it

Kernelized Wasserstein Natural Gradient
Many machine learning problems can be expressed as the optimization of s...
10/21/2019 ∙ by Michael Arbel, et al. ∙ 11 ∙ shareread it

On gradient regularizers for MMD GANs
We propose a principled method for gradientbased regularization of the ...
05/29/2018 ∙ by Michael Arbel, et al. ∙ 6 ∙ shareread it

A Kernel Stein Test for Comparing Latent Variable Models
We propose a nonparametric, kernelbased test to assess the relative goo...
07/01/2019 ∙ by Heishiro Kanagawa, et al. ∙ 6 ∙ shareread it

Counterfactual Distribution Regression for Structured Inference
We consider problems in which a system receives external perturbations f...
08/20/2019 ∙ by Nicolo Colombo, et al. ∙ 5 ∙ shareread it

Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum loglikelihood estimation for exponenti...
11/06/2018 ∙ by Bo Dai, et al. ∙ 4 ∙ shareread it

Antithetic and Monte Carlo kernel estimators for partial rankings
In the modern age, rankings data is ubiquitous and it is useful for a va...
07/01/2018 ∙ by María Lomelí, et al. ∙ 2 ∙ shareread it

Kernel Conditional Exponential Family
A nonparametric family of conditional distributions is introduced, which...
11/15/2017 ∙ by Michael Arbel, et al. ∙ 0 ∙ shareread it

Efficient and principled score estimation with Nyström kernel exponential families
We propose a fast method with statistical guarantees for learning an exp...
05/23/2017 ∙ by Dougal J. Sutherland, et al. ∙ 0 ∙ shareread it

A LinearTime Kernel GoodnessofFit Test
We propose a novel adaptive test of goodnessoffit, with computational ...
05/22/2017 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ shareread it

Fast NonParametric Tests of Relative Dependency and Similarity
We introduce two novel nonparametric statistical hypothesis tests. The ...
11/17/2016 ∙ by Wacha Bounliphone, et al. ∙ 0 ∙ shareread it

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
We propose a method to optimize the representation and distinguishabilit...
11/14/2016 ∙ by Dougal J. Sutherland, et al. ∙ 0 ∙ shareread it

An Adaptive Test of Independence with Analytic Kernel Embeddings
A new computationally efficient dependence measure, and an adaptive stat...
10/15/2016 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ shareread it

LargeScale Kernel Methods for Independence Testing
Representations of probability measures in reproducing kernel Hilbert sp...
06/25/2016 ∙ by Qinyi Zhang, et al. ∙ 0 ∙ shareread it

Interpretable Distribution Features with Maximum Testing Power
Two semimetrics on probability distributions are proposed, given as the ...
05/22/2016 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ shareread it

Recovery of nonlinear causeeffect relationships from linearly mixed neuroimaging data
Causal inference concerns the identification of causeeffect relationshi...
05/02/2016 ∙ by Sebastian Weichwald, et al. ∙ 0 ∙ shareread it

A Kernel Test for ThreeVariable Interactions with Random Processes
We apply a wild bootstrap method to the Lancaster threevariable interac...
03/02/2016 ∙ by Paul K. Rubenstein, et al. ∙ 0 ∙ shareread it

A Kernel Test of Goodness of Fit
We propose a nonparametric statistical test for goodnessoffit: given a...
02/09/2016 ∙ by Kacper Chwialkowski, et al. ∙ 0 ∙ shareread it

MERLiN: Mixture Effect Recovery in Linear Networks
Causal inference concerns the identification of causeeffect relationshi...
12/03/2015 ∙ by Sebastian Weichwald, et al. ∙ 0 ∙ shareread it

A Test of Relative Similarity For Model Selection in Generative Models
Probabilistic generative models provide a powerful framework for represe...
11/14/2015 ∙ by Wacha Bounliphone, et al. ∙ 0 ∙ shareread it

Fast TwoSample Testing with Analytic Representations of Probability Measures
We propose a class of nonparametric twosample tests with a cost linear ...
06/15/2015 ∙ by Kacper Chwialkowski, et al. ∙ 0 ∙ shareread it

Gradientfree Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradientfree adaptiv...
06/08/2015 ∙ by Heiko Strathmann, et al. ∙ 0 ∙ shareread it

KernelBased JustInTime Learning for Passing Expectation Propagation Messages
We propose an efficient nonparametric strategy for learning a message op...
03/09/2015 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ shareread it

A simpler condition for consistency of a kernel independence test
A statistical test of independence may be constructed using the Hilbert...
01/25/2015 ∙ by Arthur Gretton, et al. ∙ 0 ∙ shareread it

Passing Expectation Propagation Messages with Kernel Methods
We propose to learn a kernelbased message operator which takes as input...
01/02/2015 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ shareread it

GPselect: Accelerating EM using adaptive subspace preselection
We propose a nonparametric procedure to achieve fast inference in genera...
12/10/2014 ∙ by Jacquelyn A. Shelton, et al. ∙ 0 ∙ shareread it

Learning Theory for Distribution Regression
We focus on the distribution regression problem: regressing to vectorva...
11/08/2014 ∙ by Zoltan Szabo, et al. ∙ 0 ∙ shareread it

Kernel Mean Shrinkage Estimators
A mean function in a reproducing kernel Hilbert space (RKHS), or a kerne...
05/21/2014 ∙ by Krikamol Muandet, et al. ∙ 0 ∙ shareread it

A Kernel Independence Test for Random Processes
A new non parametric approach to the problem of testing the independence...
02/18/2014 ∙ by Kacper Chwialkowski, et al. ∙ 0 ∙ shareread it

Twostage Sampled Learning Theory on Distributions
We focus on the distribution regression problem: regressing to a realva...
02/07/2014 ∙ by Zoltan Szabo, et al. ∙ 0 ∙ shareread it

Filtering with StateObservation Examples via Kernel Monte Carlo Filter
This paper addresses the problem of filtering with a statespace model. ...
12/17/2013 ∙ by Motonobu Kanagawa, et al. ∙ 0 ∙ shareread it

Density Estimation in Infinite Dimensional Exponential Families
In this paper, we consider an infinite dimensional exponential family, P...
12/12/2013 ∙ by Bharath Sriperumbudur, et al. ∙ 0 ∙ shareread it

Hilbert Space Embeddings of Predictive State Representations
Predictive State Representations (PSRs) are an expressive class of model...
09/26/2013 ∙ by Byron Boots, et al. ∙ 0 ∙ shareread it

Kernel Adaptive MetropolisHastings
A Kernel Adaptive MetropolisHastings algorithm is introduced, for the p...
07/19/2013 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ shareread it

Btests: Low Variance Kernel TwoSample Tests
A family of maximum mean discrepancy (MMD) kernel twosample tests is in...
07/08/2013 ∙ by Wojciech Zaremba, et al. ∙ 0 ∙ shareread it

A Kernel Test for ThreeVariable Interactions
We introduce kernel nonparametric tests for Lancaster threevariable int...
06/10/2013 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ shareread it

Kernel Mean Estimation and Stein's Effect
A mean function in reproducing kernel Hilbert space, or a kernel mean, i...
06/04/2013 ∙ by Krikamol Muandet, et al. ∙ 0 ∙ shareread it

Hilbert Space Embeddings of POMDPs
A nonparametric approach for policy learning for POMDPs is proposed. The...
10/16/2012 ∙ by Yu Nishiyama, et al. ∙ 0 ∙ shareread it

Equivalence of distancebased and RKHSbased statistics in hypothesis testing
We provide a unifying framework linking two classes of statistics used i...
07/25/2012 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ shareread it

Conditional mean embeddings as regressors  supplementary
We demonstrate an equivalence between reproducing kernel Hilbert space (...
05/21/2012 ∙ by Steffen Grunewalder, et al. ∙ 0 ∙ shareread it

Hypothesis testing using pairwise distances and associated kernels (with Appendix)
We provide a unifying framework linking two classes of statistics used i...
05/02/2012 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ shareread it

Kernel Bayes' rule
A nonparametric kernelbased method for realizing Bayes' rule is propose...
09/29/2010 ∙ by Kenji Fukumizu, et al. ∙ 0 ∙ shareread it

Demystifying MMD GANs
We investigate the training and performance of generative adversarial ne...
01/04/2018 ∙ by Mikołaj Bińkowski, et al. ∙ 0 ∙ shareread it

A Generative Deep Recurrent Model for Exchangeable Data
We present a novel model architecture which leverages deep learning tool...
02/21/2018 ∙ by Iryna Korshunova, et al. ∙ 0 ∙ shareread it

Informative Features for Model Comparison
Given two candidate models, and a set of target observations, we address...
10/27/2018 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ shareread it

A maximummeandiscrepancy goodnessoffit test for censored data
We introduce a kernelbased goodnessoffit test for censored data, wher...
10/09/2018 ∙ by Tamara Fernández, et al. ∙ 0 ∙ shareread it

Maximum Mean Discrepancy Gradient Flow
We construct a Wasserstein gradient flow of the maximum mean discrepancy...
06/11/2019 ∙ by Michael Arbel, et al. ∙ 0 ∙ shareread it