-
Article
Open AccessEstimation with infinite-dimensional exponential family and Fisher divergence
Infinite dimensional exponential families have been theoretically studied, but their practical applications are still limited because empirical estimation is not straightforward. This paper first gives a brief...
-
Chapter and Conference Paper
A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective?
Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks. The most significant advantage of usi...
-
Article
Open AccessModel-based kernel sum rule: kernel Bayesian inference with probabilistic models
Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric man...
-
Article
Open AccessRobust Bayesian model selection for variable clustering with the Gaussian graphical model
Variable clustering is important for explanatory analysis. However, only few dedicated methods for variable clustering with the Gaussian graphical model have been proposed. Even more severe, small insignifican...
-
Article
Open AccessConvergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings
This paper presents convergence analysis of kernel-based quadrature rules in misspecified settings, focusing on deterministic quadrature in Sobolev spaces. In particular, we deal with misspecified settings whe...
-
Chapter and Conference Paper
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning
Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair ...
-
Article
Multilocus phylogenetic analysis with gene tree clustering
Both theoretical and empirical evidence point to the fact that phylogenetic trees of different genes (loci) do not display precisely matched topologies. Nonetheless, most genes do display related phylogenies; ...
-
Chapter and Conference Paper
From Black-Box to White-Box: Interpretable Learning with Kernel Machines
We present a novel approach to interpretable learning with kernel machines. In many real-world learning tasks, kernel machines have been successfully applied. However, a common perception is that they are diff...
-
Article
Unsupervised group matching with application to cross-lingual topic matching without alignment information
We propose a method for unsupervised group matching, which is the task of finding correspondence between groups across different domains without cross-domain similarity measurements or paired data. For example...
-
Article
Open AccessLearning sparse structural changes in high-dimensional Markov networks
Recent years have seen an increasing popularity of learning the sparse changes in Markov Networks. Changes in the structure of Markov Networks reflect alternations of interactions between random variables under ...
-
Article
Open AccessComputing functions of random variables via reproducing kernel Hilbert space representations
We describe a method to perform functional operations on probability distributions of random variables. The method uses reproducing kernel Hilbert space representations of probability distributions, and it is ...
-
Chapter and Conference Paper
Reducing Hubness for Kernel Regression
In this paper, we point out that hubness—some samples in a high-dimensional dataset emerge as hubs that are similar to many other samples—influences the performance of kernel regression. Because the dimension of ...
-
Chapter
Nonparametric Bayesian Inference with Kernel Mean Embedding
Kernel methods have been successfully used in many machine learning problems with favorable performance in extracting nonlinear structure of high-dimensional data. Recently, nonparametric inference methods wit...
-
Article
Parameter estimation for von Mises–Fisher distributions
When analyzing high-dimensional data, it is often appropriate to pay attention only to the direction of each datum, disregarding its norm. The von Mises–Fisher (vMF) distribution is a natural probability distr...
-
Article
Preface
-
Chapter and Conference Paper
An Efficient Learning Algorithm Using Natural Gradient and Second Order Information of Error Surface
Natural gradient learning algorithm, which originated from information geometry, is known to provide a good solution for the problem of slow learning speed of gradient descent learning methods. Whereas the nat...
-
Chapter and Conference Paper
Generalization Error of Linear Neural Networks in Unidentifiable Cases
The statistical asymptotic theory is often used in theoretical results in computational and statistical learning theory. It describes the limiting distribution of the maximum likelihood estimator (MLE) as an n...
-
Chapter and Conference Paper
Dynamics of Batch Learning in Multilayer Neural Networks
We discuss the dynamics of batch learning of multilayer neural networks in the asymptotic limit, where the number of trining data is much larger than the number of parameters, emphasizing on the parameterizati...
