Statistical Test for Feature Selection Pipelines by Selective Inference

Tomohiro Shiraishi, Tatsuya Matsukawa, Shuichi Nishino, Ichiro Takeuchi

International Conference on Machine Learning 2025 · Oral

In the rapidly evolving landscape of AI-driven scientific discovery, the ability to identify truly meaningful patterns amidst vast datasets is paramount. This talk, presented by Ichiro Takeuchi from Nagoya University and RIKEN, Japan, along with his students Tomohiro Shiraishi, Tatsuya Matsukawa, and Shuichi Nishino, addresses a critical challenge in this domain: ensuring the reliability of discoveries made by powerful AI models. Specifically, the presentation introduces a novel framework for conducting **statistical tests** on **feature selection pipelines** by leveraging **selective inference**.

AI review

A technically credible extension of selective inference to modular feature selection pipelines, with a clean analogy between auto-differentiation and auto-conditioning as the organizing idea. The Type I error control guarantee is real and useful. However, based on this article, the contribution reads as a solid engineering of known machinery rather than a conceptual breakthrough: the underlying selective inference theory is classical (Lee et al., Fithian et al., Tibshirani et al.'s polyhedral lemma work), and the main novelty is a compositional bookkeeping scheme for selection events. The…