Distributional Extrapolation for Interactions

2026-08-20

AI Summary

研究通过引入DExtrI方法解决从有限范围观测预测组合效应的问题，该方法能够成功推断训练数据支持之外的交互作用。

Abstract

Predicting combinatorial effects from limited-range observations is a fundamental challenge in many scientific domains, including drug discovery and hyperparameter optimization. We study combinatorial extrapolation, where training data consists of axis-aligned samples with only one active covariate, while test-time inputs involve multiple simultaneously active covariates. We introduce DExtrI, a method for extrapolating interaction effects beyond the support of the training data. We provide theoretical guarantees characterizing when such extrapolation is possible. Empirical results on synthetic and real-world datasets demonstrate that DExtrI successfully generalizes to unseen combinations of covariates. Our approach enables applications such as predicting previously untested drug combinations and improving the efficiency of hyperparameter optimization.

Problem

• combinatorial effects
• limited-range observations
• interaction effects
• extrapolation

Innovation

• DExtrI
• combinatorial extrapolation
• interaction effects