交互作用的分布外推
Distributional Extrapolation for Interactions
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中文总结 AI 辅助
针对从有限观测预测组合效应的挑战，提出DExtrI方法实现交互效应外推，经理论保证与实验验证可泛化至未见过的协变量组合，支撑药物组合预测与超参数优化等应用。
中文摘要 AI 辅助
从有限范围的观测中预测组合效应是药物发现、超参数优化等众多科学领域的核心挑战。我们研究组合外推问题，其中训练数据由仅含一个活跃协变量的轴对齐样本构成，而测试阶段输入则包含多个同时活跃的协变量。我们提出DExtrI方法，用于将交互效应外推至训练数据支持范围之外，并提供了此类外推可行的理论保证。在合成数据集和真实世界数据集上的实验结果表明，DExtrI可成功泛化至未见过的协变量组合，该方法可应用于预测此前未测试的药物组合、提升超参数优化效率等场景。
英文摘要
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.
