4.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of TokyoJP154 papers
- National Astronomical Observatory of JapanJP111 papers
- The Graduate University for Advanced Studies, SOKENDAIJP101 papers
- Kavli Institute for the Physics and Mathematics of the UniverseJP67 papers
- Nagoya UniversityJP57 papers
- Hiroshima UniversityJP47 papers
- Centre National de la Recherche ScientifiqueFR45 papers
- Tohoku UniversityJP42 papers
- University College LondonGB42 papers
- Institute of Astronomy and Astrophysics, Academia SinicaTW41 papers
- Korea Astronomy and Space Science InstituteKR41 papers
- Tokyo Institute of TechnologyJP41 papers
36 papers · 1 filter
Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
Teng Li, Stephen Wu, Yong Huang +2
The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. Inferring these states from meas…
Gaussian Process Koopman Mode Decomposition
Takahiro Kawashima, Hideitsu Hino
In this paper, we propose a nonlinear probabilistic generative model of Koopman mode decomposition based on an unsupervised Gaussian process. Existing data-driven methods for Koopm…
Prediction Errors for Penalized Regressions based on Generalized Approximate Message Passing
Ayaka Sakata
We discuss the prediction accuracy of assumed statistical models in terms of prediction errors for the generalized linear model and penalized maximum likelihood methods. We derive…
Stopping Criterion for Active Learning Based on Error Stability
Hideaki Ishibashi, Hideitsu Hino
Active learning is a framework for supervised learning to improve the predictive performance by adaptively annotating a small number of samples. To realize efficient active learnin…
Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis
Kazuharu Harada, Hironori Fujisawa
We consider the problem of inferring the causal structure from observational data, especially when the structure is sparse. This type of problem is usually formulated as an inferen…
Active pooling design in group testing based on Bayesian posterior prediction
Ayaka Sakata
In identifying infected patients in a population, group testing is an effective method to reduce the number of tests and correct the test errors. In the group testing procedure, te…
Bayesian inference of infected patients in group testing with prevalence estimation
Ayaka Sakata
Group testing is a method of identifying infected patients by performing tests on a pool of specimens collected from patients. For the case in which the test returns a false result…
A Bayesian algorithm for retrosynthesis
Zhongliang Guo, Stephen Wu, Mitsuru Ohno +1
The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limit…
Recreation of the Periodic Table with an Unsupervised Machine Learning Algorithm
Minoru Kusaba, Chang Liu, Yukinori Koyama +2
In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of…
A Kernel Stein Test for Comparing Latent Variable Models
Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey +2
We propose a kernel-based nonparametric test of relative goodness of fit, where the goal is to compare two models, both of which may have unobserved latent variables, such that the…
Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration
Tomoyuki Obuchi, Ayaka Sakata
We investigate the signal reconstruction performance of sparse linear regression in the presence of noise when piecewise continuous nonconvex penalties are used. Among such penalti…
HMLasso: Lasso with High Missing Rate
Masaaki Takada, Hironori Fujisawa, Takeichiro Nishikawa
Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often…
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai +3
Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection,…
Robust and Sparse Regression in GLM by Stochastic Optimization
Takayuki Kawashima, Hironori Fujisawa
The generalized linear model (GLM) plays a key role in regression analyses. In high-dimensional data, the sparse GLM has been used but it is not robust against outliers. Recently,…
Estimator of Prediction Error Based on Approximate Message Passing for Penalized Linear Regression
Ayaka Sakata
We propose an estimator of prediction error using an approximate message passing (AMP) algorithm that can be applied to a broad range of sparse penalties. Following Stein's lemma,…
Deep Neural Networks Learn Non-Smooth Functions Effectively
Masaaki Imaizumi, Kenji Fukumizu
We theoretically discuss why deep neural networks (DNNs) performs better than other models in some cases by investigating statistical properties of DNNs for non-smooth functions. W…
Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis
Ayaka Sakata, Yingying Xu
We analyse a linear regression problem with nonconvex regularization called smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis for Gaussian random data…
Exhaustive search for sparse variable selection in linear regression
Yasuhiko Igarashi, Hikaru Takenaka, Yoshinori Nakanishi-Ohno +3
We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods,…
Kernel method for persistence diagrams via kernel embedding and weight factor
Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka
Topological data analysis is an emerging mathematical concept for characterizing shapes in multi-scale data. In this field, persistence diagrams are widely used as a descriptor of…
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang
Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CC…
A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo +2
We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples. We learn the test features that best indicate the differences between…
Trimmed Density Ratio Estimation
Song Liu, Akiko Takeda, Taiji Suzuki +1
Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation procedure can be…
Sparse principal component regression for generalized linear models
Shuichi Kawano, Hironori Fujisawa, Toyoyuki Takada +1
Principal component regression (PCR) is a widely used two-stage procedure: principal component analysis (PCA), followed by regression in which the selected principal components are…
Kernel Mean Embedding of Distributions: A Review and Beyond
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur +1
A Hilbert space embedding of a distribution---in short, a kernel mean embedding---has recently emerged as a powerful tool for machine learning and inference. The basic idea behind…