4 citations · 11 across the 13 of their papers we have counts for
14 papers
Covariate-dependent Nonparametric -modeling for regression via infinite Mixture-of-Expertizing class
Akira Okazaki, Keisuke Yano
Empirical Bayes -modeling captures unit-level heterogeneity by estimating a latent prior distribution from observed data. In the existing formulations, however, the prior is sha…
Dual Riemannian Newton Method on Statistical Manifolds
Derun Zhou, Keisuke Yano, Mahito Sugiyama
In probabilistic modeling, parameter estimation is commonly formulated as a minimization problem on a parameter manifold. Optimization in such spaces requires geometry-aware method…
Minimum information dependence modeling
Tomonari Sei, Keisuke Yano
We propose a method to construct a joint statistical model for mixed-domain data to analyze their dependence. Multivariate Gaussian and log-linear models are particular examples of…
A generalization gap estimation for overparameterized models via the Langevin functional variance
Akifumi Okuno, Keisuke Yano
This paper discusses the estimation of the generalization gap, the difference between generalization performance and training performance, for overparameterized models including ne…
Posterior Covariance Information Criterion for Weighted Inference
Yukito Iba, Keisuke Yano
For predictive evaluation based on quasi-posterior distributions, we develop a new information criterion, the posterior covariance information criterion (PCIC. PCIC generalises the…
Structured regularization based velocity structure estimation in local earthquake tomography for the adaptation to velocity discontinuities
Yohta Yamanaka, Sumito Kurata, Keisuke Yano +3
We propose a local earthquake tomography method that applies a structured regularization technique to determine sharp changes in Earth's seismic velocity structure using arrival ti…
Dependence of variance on covariate design in nonparametric link regression
Akifumi Okuno, Keisuke Yano
This paper discusses a design-dependent nature of variance in nonparametric link regression aiming at predicting a mean outcome at a link, i.e., a pair of nodes, based on currently…
Learning partially ranked data based on graph regularization
Kento Nakamura, Keisuke Yano, Fumiyasu Komaki
Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data…
Minimax Predictive Density for Sparse Count Data
Keisuke Yano, Ryoya Kaneko, Fumiyasu Komaki
This paper discusses predictive densities under the Kullback--Leibler loss for high-dimensional Poisson sequence models under sparsity constraints. Sparsity in count data implies z…
On frequentist coverage errors of Bayesian credible sets in moderately high dimensions
Keisuke Yano, Kengo Kato
In this paper, we study frequentist coverage errors of Bayesian credible sets for an approximately linear regression model with (moderately) high dimensional regressors, where the…
On -Admissibility in High Dimension and Nonparametrics
Keisuke Yano, Fumiyasu Komaki
In this paper, we discuss the use of -admissibility for estimation in high-dimensional and nonparametric statistical models. The minimax rate of convergence is widely…
Non-asymptotic Bayesian Minimax Adaptation
Keisuke Yano, Fumiyasu Komaki
This paper studies a Bayesian approach to non-asymptotic minimax adaptation in nonparametric estimation. Estimating an input function on the basis of output functions in a Gaussian…
Asymptotically Minimax Prediction in Infinite Sequence Models
Keisuke Yano, Fumiyasu Komaki
We study asymptotically minimax predictive distributions in an infinite sequence model. First, we discuss the connection between the prediction in the infinite sequence model and t…
Information criteria for multistep ahead predictions
Keisuke Yano, Fumiyasu Komaki
We propose an information criterion for multistep ahead predictions. It is also used for extrapolations. For the derivation, we consider multistep ahead predictions under local mis…