75 citations · 459 across the 65 of their papers we have counts for
7 papers · 2 filters
More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach
Kazuya Sugiyama, Vo Nguyen Le Duy, Ichiro Takeuchi
Conditional selective inference (SI) has been actively studied as a new statistical inference framework for data-driven hypotheses. The basic idea of conditional SI is to make infe…
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
Vo Nguyen Le Duy, Shogo Iwazaki, Ichiro Takeuchi
Although a vast body of literature relates to image segmentation methods that use deep neural networks (DNNs), less attention has been paid to assessing the statistical reliability…
Mean-Variance Analysis in Bayesian Optimization under Uncertainty
Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi
We consider active learning (AL) in an uncertain environment in which trade-off between multiple risk measures need to be considered. As an AL problem in such an uncertain environm…
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure
Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi
In many product development problems, the performance of the product is governed by two types of parameters called design parameter and environmental parameter. While the former is…
Parametric Programming Approach for More Powerful and General Lasso Selective Inference
Vo Nguyen Le Duy, Ichiro Takeuchi
Selective Inference (SI) has been actively studied in the past few years for conducting inference on the features of linear models that are adaptively selected by feature selection…
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
Vo Nguyen Le Duy, Hiroki Toda, Ryota Sugiyama +1
There is a vast body of literature related to methods for detecting changepoints (CP). However, less attention has been paid to assessing the statistical reliability of the detecte…