Tatsuya Matsukawa — accepted AI-conference papers

2025

Statistical Test for Auto Feature Engineering by Selective Inference

Tatsuya Matsukawa, Tomohiro Shiraishi, Shuichi Nishino, Teruyuki Katsuoka, Ichiro Takeuchi

AISTATS 2025poster

Auto Feature Engineering (AFE) plays a crucial role in developing practical machine learning pipelines by automating the transformation of raw data into meaningful features that enhance model performance. By generating features in a data-driven manner, AFE enables the discovery of important features…

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2025

Statistical Test for Feature Selection Pipelines by Selective Inference

Tomohiro Shiraishi, Tatsuya Matsukawa, Shuichi Nishino, Ichiro Takeuchi

ICML 2025oral

A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating various analysis algorithms. In this paper, we propose a novel statistical test to assess the significance of data analysis pipelines. Our approach enables the systematic devel…

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