Yu Inatsu

发表

Model selection criterion based on the prediction mean squared error in generalized estimating equations

Yu Inatsu, Shinpei Imori, S. Imori, 2018, Hiroshima Mathematical Journal.

Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design

H. Yawo, I. Takeuchi, Daichi Yamada, 2020, Communications Biology.

Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation pdf

Ichiro Takeuchi, Vo Nguyen Le Duy, Yu Inatsu, 2021, Annals of the Institute of Statistical Mathematics.

Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference pdf

Ichiro Takeuchi, Hiroki Toda, Vo Nguyen Le Duy, 2021, ArXiv.

Computing Valid P-Values for Image Segmentation by Selective Inference

Hidekata Hontani, Ichiro Takeuchi, Noriaki Hashimoto, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

A $C_p$ type criterion for model selection in the GEE method when both scale and correlation parameters are unknown

Yu Inatsu, Tomoharu Sato, 2020 .

Model Selection Criteria for ANOVA Model with a Tree Order Restriction

Yu Inatsu, 2016 .

Asymptotic Expansions of the Null Distribution of the LR Test Statistic for Random-Effects Covariance Structure in a Parallel Profile Model

H. Wakaki, Yu Inatsu, 2016 .

Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design

H. Yawo, I. Takeuchi, Daichi Yamada, 2021, Communications Biology.

Active learning for distributionally robust level-set estimation pdf

Ichiro Takeuchi, Yu Inatsu, Shogo Iwazaki, 2021, ICML.

Bayesian Quadrature Optimization for Probability Threshold Robustness Measure pdf

Ichiro Takeuchi, Yu Inatsu, Shogo Iwazaki, 2020, Neural Computation.

Randomized Gaussian Process Upper Confidence Bound with Tight Bayesian Regret Bounds

Yu Inatsu, Masayuki Karasuyama, Shion Takeno, 2023, ICML.

Bayesian Optimization for Cascade-Type Multistage Processes

I. Takeuchi, Yu Inatsu, Masayuki Karasuyama, 2021, Neural Computation.

Bayesian Optimization for Distributionally Robust Chance-constrained Problem

I. Takeuchi, Yu Inatsu, Masayuki Karasuyama, 2022, ICML.

Mean-Variance Analysis in Bayesian Optimization under Uncertainty pdf

Ichiro Takeuchi, Yu Inatsu, Shogo Iwazaki, 2020, AISTATS.

Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions

Ichiro Takeuchi, Masayuki Karasuyama, Keiichi Inoue, 2020, Neural Computation.

Bayesian Experimental Design for Finding Reliable Level Set Under Input Uncertainty

Ichiro Takeuchi, Yu Inatsu, Shogo Iwazaki, 2019, IEEE Access.

Active learning for level set estimation under cost-dependent input uncertainty pdf

Ichiro Takeuchi, Masayuki Karasuyama, Keiichi Inoue, 2019, ArXiv.

Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives

Ichiro Takeuchi, Yu Inatsu, Daisuke Sugita, 2019, Neural Computation.

Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation

Ichiro Takeuchi, Masayuki Karasuyama, Keiichi Inoue, 2020, Neural Computation.

An unbiased Cp type criterion for ANOVA model with a tree order restriction

Yu Inatsu, 2017 .

Computing Valid P-Values for Image Segmentation by Selective Inference

I. Takeuchi, Yu Inatsu, H. Hontani, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).