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More Powerful and General Selective Inference for Stepwise Feature
  Selection using the Homotopy Continuation Approach

More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach

25 December 2020
Kazuya Sugiyama
Vo Nguyen Le Duy
Ichiro Takeuchi
ArXivPDFHTML

Papers citing "More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach"

6 / 6 papers shown
Title
Valid P-Value for Deep Learning-Driven Salient Region
Valid P-Value for Deep Learning-Driven Salient Region
Daiki Miwa
Vo Nguyen Le Duy
I. Takeuchi
FAtt
AAML
37
15
0
06 Jan 2023
Exact Statistical Inference for the Wasserstein Distance by Selective
  Inference
Exact Statistical Inference for the Wasserstein Distance by Selective Inference
Vo Nguyen Le Duy
Ichiro Takeuchi
51
14
0
29 Sep 2021
More Powerful Conditional Selective Inference for Generalized Lasso by
  Parametric Programming
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming
Vo Nguyen Le Duy
Ichiro Takeuchi
45
34
0
11 May 2021
Conditional Selective Inference for Robust Regression and Outlier
  Detection using Piecewise-Linear Homotopy Continuation
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation
Toshiaki Tsukurimichi
Yu Inatsu
Vo Nguyen Le Duy
Ichiro Takeuchi
57
22
0
22 Apr 2021
Quantifying Statistical Significance of Neural Network-based Image
  Segmentation by Selective Inference
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
Vo Nguyen Le Duy
S. Iwazaki
Ichiro Takeuchi
32
18
0
05 Oct 2020
Confidence Sets Based on Penalized Maximum Likelihood Estimators in
  Gaussian Regression
Confidence Sets Based on Penalized Maximum Likelihood Estimators in Gaussian Regression
B. M. Potscher
U. Schneider
119
54
0
10 Jun 2008
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