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04/04/2020
The equivalence between Stein variational gradient descent and black-box variational inference
We formalize an equivalence between two popular methods for Bayesian inf...
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02/11/2020
Smoothness and Stability in GANs
Generative adversarial networks, or GANs, commonly display unstable beha...
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01/07/2020
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
We address the problem of distinguishing cause from effect in bivariate ...
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01/01/2020
Statistical Invariance of Betti Numbers in the Thermodynamic Regime
Topological Data Analysis (TDA) has become a promising tool to uncover l...
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10/22/2019
Deep Set-to-Set Matching and Learning
Matching two sets of items, called set-to-set matching problem, is being...
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08/24/2019
Disjunct Support Spike and Slab Priors for Variable Selection in Regression under Quasi-sparseness
Sparseness of the regression coefficient vector is often a desirable pro...
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08/24/2019
Disjunct Support Spike and Slab Priors for Variable Selection in Regression
Sparseness of the regression coefficient vector is often a desirable pro...
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07/01/2019
A Kernel Stein Test for Comparing Latent Variable Models
We propose a nonparametric, kernel-based test to assess the relative goo...
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06/12/2019
Semi-flat minima and saddle points by embedding neural networks to overparameterization
We theoretically study the landscape of the training error for neural ne...
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03/05/2019
Convex Covariate Clustering for Classification
Clustering, like covariate selection for classification, is an important...
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02/01/2019
Tree-Sliced Approximation of Wasserstein Distances
Optimal transport () theory provides a useful set of tools to compare pr...
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09/04/2018
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
In this paper, we propose a new kernel-based co-occurrence measure that ...
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06/15/2018
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model
Variable clustering is important for explanatory analysis. However, only...
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05/22/2018
Variational Learning on Aggregate Outputs with Gaussian Processes
While a typical supervised learning framework assumes that the inputs an...
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02/23/2018
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
We propose a novel approach to parameter estimation for simulator-based ...
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02/17/2018
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
Measuring divergence between two distributions is essential in machine l...
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02/15/2018
Selecting the Best in GANs Family: a Post Selection Inference Framework
"Which Generative Adversarial Networks (GANs) generates the most plausib...
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02/13/2018
Deep Neural Networks Learn Non-Smooth Functions Effectively
We theoretically discuss why deep neural networks (DNNs) performs better...
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09/01/2017
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings
This paper presents convergence analysis of kernel-based quadrature rule...
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06/12/2017
Kernel method for persistence diagrams via kernel embedding and weight factor
Topological data analysis is an emerging mathematical concept for charac...
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05/22/2017
A Linear-Time Kernel Goodness-of-Fit Test
We propose a novel adaptive test of goodness-of-fit, with computational ...
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05/09/2017
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Many unsupervised kernel methods rely on the estimation of the kernel co...
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03/09/2017
Trimmed Density Ratio Estimation
Density ratio estimation is a vital tool in both machine learning and st...
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01/06/2017
Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories
Recent years have seen an increasing popularity of learning the sparse c...
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10/12/2016
Post Selection Inference with Kernels
We propose a novel kernel based post selection inference (PSI) algorithm...
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05/31/2016
Kernel Mean Embedding of Distributions: A Review and Beyond
A Hilbert space embedding of a distribution---in short, a kernel mean em...
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05/24/2016
Convergence guarantees for kernel-based quadrature rules in misspecified settings
Kernel-based quadrature rules are becoming important in machine learning...
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02/17/2016
Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods
To the best of our knowledge, there are no general well-founded robust m...
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06/09/2015
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective
Transfer learning assumes classifiers of similar tasks share certain par...
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04/02/2015
Structure Learning of Partitioned Markov Networks
We learn the structure of a Markov Network between two groups of random ...
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01/27/2015
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability dis...
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07/02/2014
Support Consistency of Direct Sparse-Change Learning in Markov Networks
We study the problem of learning sparse structure changes between two Ma...
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05/21/2014
Kernel Mean Shrinkage Estimators
A mean function in a reproducing kernel Hilbert space (RKHS), or a kerne...
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03/28/2014
Characteristic Kernels and Infinitely Divisible Distributions
We connect shift-invariant characteristic kernels to infinitely divisibl...
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12/17/2013
Filtering with State-Observation Examples via Kernel Monte Carlo Filter
This paper addresses the problem of filtering with a state-space model. ...
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12/12/2013
Density Estimation in Infinite Dimensional Exponential Families
In this paper, we consider an infinite dimensional exponential family, P...
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06/04/2013
Kernel Mean Estimation and Stein's Effect
A mean function in reproducing kernel Hilbert space, or a kernel mean, i...
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10/16/2012
Hilbert Space Embeddings of POMDPs
A nonparametric approach for policy learning for POMDPs is proposed. The...
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07/25/2012
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
We provide a unifying framework linking two classes of statistics used i...
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05/02/2012
Hypothesis testing using pairwise distances and associated kernels (with Appendix)
We provide a unifying framework linking two classes of statistics used i...
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02/29/2012
Learning from Distributions via Support Measure Machines
This paper presents a kernel-based discriminative learning framework on ...
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09/02/2011
Gradient-based kernel dimension reduction for supervised learning
This paper proposes a novel kernel approach to linear dimension reductio...
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03/03/2011
Loopy Belief Propagation, Bethe Free Energy and Graph Zeta Function
We propose a new approach to the theoretical analysis of Loopy Belief Pr...
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09/29/2010
Kernel Bayes' rule
A nonparametric kernel-based method for realizing Bayes' rule is propose...
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02/17/2010