
TreeSliced Approximation of Wasserstein Distances
Optimal transport () theory provides a useful set of tools to compare pr...
02/01/2019 ∙ by Tam Le, et al. ∙ 12 ∙ 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

Kernel method for persistence diagrams via kernel embedding and weight factor
Topological data analysis is an emerging mathematical concept for charac...
06/12/2017 ∙ by Genki Kusano, 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

Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Many unsupervised kernel methods rely on the estimation of the kernel co...
05/09/2017 ∙ by Md. Ashad Alam, et al. ∙ 0 ∙ shareread it

Trimmed Density Ratio Estimation
Density ratio estimation is a vital tool in both machine learning and st...
03/09/2017 ∙ by Song Liu, et al. ∙ 0 ∙ shareread it

Learning Sparse Structural Changes in Highdimensional Markov Networks: A Review on Methodologies and Theories
Recent years have seen an increasing popularity of learning the sparse c...
01/06/2017 ∙ by Song Liu, et al. ∙ 0 ∙ shareread it

Post Selection Inference with Kernels
We propose a novel kernel based post selection inference (PSI) algorithm...
10/12/2016 ∙ by Makoto Yamada, et al. ∙ 0 ∙ shareread it

Kernel Mean Embedding of Distributions: A Review and Beyond
A Hilbert space embedding of a distributionin short, a kernel mean em...
05/31/2016 ∙ by Krikamol Muandet, et al. ∙ 0 ∙ shareread it

Convergence guarantees for kernelbased quadrature rules in misspecified settings
Kernelbased quadrature rules are becoming important in machine learning...
05/24/2016 ∙ by Motonobu Kanagawa, et al. ∙ 0 ∙ shareread it

Robust Kernel (Cross) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods
To the best of our knowledge, there are no general wellfounded robust m...
02/17/2016 ∙ by Md. Ashad Alam, et al. ∙ 0 ∙ shareread it

Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective
Transfer learning assumes classifiers of similar tasks share certain par...
06/09/2015 ∙ by Song Liu, et al. ∙ 0 ∙ shareread it

Structure Learning of Partitioned Markov Networks
We learn the structure of a Markov Network between two groups of random ...
04/02/2015 ∙ by Song Liu, et al. ∙ 0 ∙ shareread it

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability dis...
01/27/2015 ∙ by Bernhard Schölkopf, et al. ∙ 0 ∙ shareread it

Support Consistency of Direct SparseChange Learning in Markov Networks
We study the problem of learning sparse structure changes between two Ma...
07/02/2014 ∙ by Song Liu, 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

Characteristic Kernels and Infinitely Divisible Distributions
We connect shiftinvariant characteristic kernels to infinitely divisibl...
03/28/2014 ∙ by Yu Nishiyama, 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

Loopy Belief Propagation, Bethe Free Energy and Graph Zeta Function
We propose a new approach to the theoretical analysis of Loopy Belief Pr...
03/03/2011 ∙ by Yusuke Watanabe, 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

Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation
We propose a new approach to the analysis of Loopy Belief Propagation (L...
02/17/2010 ∙ by Yusuke Watanabe, 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

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

Learning from Distributions via Support Measure Machines
This paper presents a kernelbased discriminative learning framework on ...
02/29/2012 ∙ by Krikamol Muandet, et al. ∙ 0 ∙ shareread it

Gradientbased kernel dimension reduction for supervised learning
This paper proposes a novel kernel approach to linear dimension reductio...
09/02/2011 ∙ by Kenji Fukumizu, 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

Deep Neural Networks Learn NonSmooth Functions Effectively
We theoretically discuss why deep neural networks (DNNs) performs better...
02/13/2018 ∙ by Masaaki Imaizumi, et al. ∙ 0 ∙ shareread it

Selecting the Best in GANs Family: a Post Selection Inference Framework
"Which Generative Adversarial Networks (GANs) generates the most plausib...
02/15/2018 ∙ by YaoHung Hubert Tsai, et al. ∙ 0 ∙ shareread it

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
Measuring divergence between two distributions is essential in machine l...
02/17/2018 ∙ by Makoto Yamada, et al. ∙ 0 ∙ shareread it

Kernel Recursive ABC: Point Estimation with Intractable Likelihood
We propose a novel approach to parameter estimation for simulatorbased ...
02/23/2018 ∙ by Takafumi Kajihara, et al. ∙ 0 ∙ shareread it

Convergence Analysis of Deterministic KernelBased Quadrature Rules in Misspecified Settings
This paper presents convergence analysis of kernelbased quadrature rule...
09/01/2017 ∙ by Motonobu Kanagawa, et al. ∙ 0 ∙ shareread it

Variational Learning on Aggregate Outputs with Gaussian Processes
While a typical supervised learning framework assumes that the inputs an...
05/22/2018 ∙ by Ho Chung Leon Law, et al. ∙ 0 ∙ shareread it

Pointwise HSIC: A LinearTime Kernelized Cooccurrence Norm for Sparse Linguistic Expressions
In this paper, we propose a new kernelbased cooccurrence measure that ...
09/04/2018 ∙ by Sho Yokoi, et al. ∙ 0 ∙ shareread it

Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model
Variable clustering is important for explanatory analysis. However, only...
06/15/2018 ∙ by Daniel Andrade, et al. ∙ 0 ∙ shareread it

Semiflat minima and saddle points by embedding neural networks to overparameterization
We theoretically study the landscape of the training error for neural ne...
06/12/2019 ∙ by Kenji Fukumizu, et al. ∙ 0 ∙ shareread it

Convex Covariate Clustering for Classification
Clustering, like covariate selection for classification, is an important...
03/05/2019 ∙ by Daniel Andrade, et al. ∙ 0 ∙ shareread it

Disjunct Support Spike and Slab Priors for Variable Selection in Regression
Sparseness of the regression coefficient vector is often a desirable pro...
08/24/2019 ∙ by Daniel Andrade, et al. ∙ 0 ∙ shareread it

Disjunct Support Spike and Slab Priors for Variable Selection in Regression under Quasisparseness
Sparseness of the regression coefficient vector is often a desirable pro...
08/24/2019 ∙ by Daniel Andrade, et al. ∙ 0 ∙ shareread it

Deep SettoSet Matching and Learning
Matching two sets of items, called settoset matching problem, is being...
10/22/2019 ∙ by Yuki Saito, et al. ∙ 0 ∙ shareread it