通过松弛变量提升因子分解机的高阶交互建模能力，用于药物组合效应预测。
Surrogate Modeling via Factorization Machine and Ising Model with Enhanced Higher-Order Interaction Learning
- 引入松弛变量统一优化流程，实现因子分解机与伊辛模型的一体化训练
- 在药物组合预测任务中显著提升性能，验证了高阶交互学习的有效性
- 适合关注量子计算优势的机器学习研究者及生物医学建模需求者
近期有研究提出利用因子分解机近似原系统的输入输出映射，并采用量子退火优化所得代理函数。受此启发，我们提出一种增强型代理模型，将额外的松弛变量引入因子分解机及其对应的伊辛表示，从而将原本设计为两步的过程整合为单一协同步骤。训练过程中，松弛变量被迭代更新，使模型能够更好地捕捉高阶特征交互。我们将该方法应用于药物组合效应预测任务，实验结果表明，引入松弛变量显著提升了模型性能。所提算法为构建能利用潜在量子优势的高效代理模型提供了可行路径。
原文摘要 · Abstract (English)
Recently, a surrogate model was proposed that employs a factorization machine to approximate the underlying input-output mapping of the original system, with quantum annealing used to optimize the resulting surrogate function. Inspired by this approach, we propose an enhanced surrogate model that incorporates additional slack variables into both the factorization machine and its associated Ising representation thereby unifying what was by design a two-step process into a single, integrated step. During the training phase, the slack variables are iteratively updated, enabling the model to account for higher-order feature interactions. We apply the proposed method to the task of predicting drug combination effects. Experimental results indicate that the introduction of slack variables leads to a notable improvement of performance. Our algorithm offers a promising approach for building efficient surrogate models that exploit potential quantum advantages.
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