30 citations · 63 across the 9 of their papers we have counts for
10 papers
Model Bridging: Connection between Simulation Model and Neural Network
Keiichi Kisamori, Keisuke Yamazaki, Yuto Komori +1
The interpretability of machine learning, particularly for deep neural networks, is crucial for decision making in real-world applications. One approach is replacing the un-interpr…
Simulator Calibration under Covariate Shift with Kernels
Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki
We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift. Covariate shift is the situation where input distributions for training…
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki +1
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel…
Effects of Additional Data on Bayesian Clustering
Keisuke Yamazaki
Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the…
Bayesian Estimation of Multidimensional Latent Variables and Its Asymptotic Accuracy
Keisuke Yamazaki
Hierarchical learning models, such as mixture models and Bayesian networks, are widely employed for unsupervised learning tasks, such as clustering analysis. They consist of observ…
Asymptotic Accuracy of Bayesian Estimation for a Single Latent Variable
Keisuke Yamazaki
In data science and machine learning, hierarchical parametric models, such as mixture models, are often used. They contain two kinds of variables: observable variables, which repre…
Accuracy of Latent-Variable Estimation in Bayesian Semi-Supervised Learning
Keisuke Yamazaki
Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which…
Stochastic complexity of Bayesian networks
Keisuke Yamazaki, Sumio Watanbe
Bayesian networks are now being used in enormous fields, for example, diagnosis of a system, data mining, clustering and so on. In spite of their wide range of applications, the st…
Asymptotic Accuracy of Bayes Estimation for Latent Variables with Redundancy
Keisuke Yamazaki
Hierarchical parametric models consisting of observable and latent variables are widely used for unsupervised learning tasks. For example, a mixture model is a representative hiera…
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables
Keisuke Yamazaki
Hierarchical statistical models are widely employed in information science and data engineering. The models consist of two types of variables: observable variables that represent t…