Statistically Robust Sparse High-order Interaction Model

Diptesh Das, Ichiro Takeuchi, Koji Tsuda

Keywords: Interpretability, Reliability, Trustworthy AI, High-stake decision, High-order Interaction Model, Conformal Prediction

TL;DR: This paper introduces Huberized-SHIM, an extension of the Sparse High-Order Interaction Model (SHIM) that enhances robustness against outliers while maintaining interpretability in high-stakes applications.

Abstract: Deep learning models often achieve high accuracy but lack interpretability, making them unsuitable for critical applications such as medical diagnosis, biomolecule design, criminal justice, etc. The Sparse High-Order Interaction Model (SHIM) addresses this limitation by providing both transparency and predictive reliability. However, real-world data often contain outliers, which can distort model performance. To overcome this, we propose Huberized-SHIM, an extension of SHIM that integrates Huber loss-based robust regression to mitigate the impact of outliers. We introduce a homotopy-based regularization path algorithm and a novel tree-pruning criterion to efficiently manage interaction complexity. Additionally, we incorporate the conformal prediction framework to enhance statistical reliability. Empirical evaluations on synthetic and real-world datasets demonstrate the superior robustness and accuracy of Huberized-SHIM in high-stakes decision-making contexts.

Submission Number: 19