神经可加专家：基于情境门控的可控模型可加性专家系统

摘要

可解释性与准确性的权衡始终是机器学习领域的核心挑战。标准广义可加模型（GAMs）虽能提供清晰的特征归因，但其严格的加性结构常会限制预测性能。引入特征交互可提升准确性，却可能模糊个体特征的贡献度。为解决这些问题，我们提出神经可加专家模型（NAEs）——一种在可解释性与准确性间实现无缝平衡的创新框架。NAEs采用专家混合框架，为每个特征学习多个专用网络，同时通过动态门控机制整合跨特征信息，从而突破刚性加性约束。此外，我们提出针对性正则化技术以降低专家预测间的方差，实现从纯加性模型到捕获复杂特征交互模型的平滑过渡，同时保持特征归因的清晰度。通过理论分析和合成数据实验，我们验证了该模型的灵活性；在真实数据集上的广泛评估表明，NAEs在预测准确性与透明化特征级解释之间达到了最优平衡。代码详见https://github.com/Teddy-XiongGZ/NAE。

English

The trade-off between interpretability and accuracy remains a core challenge in machine learning. Standard Generalized Additive Models (GAMs) offer clear feature attributions but are often constrained by their strictly additive nature, which can limit predictive performance. Introducing feature interactions can boost accuracy yet may obscure individual feature contributions. To address these issues, we propose Neural Additive Experts (NAEs), a novel framework that seamlessly balances interpretability and accuracy. NAEs employ a mixture of experts framework, learning multiple specialized networks per feature, while a dynamic gating mechanism integrates information across features, thereby relaxing rigid additive constraints. Furthermore, we propose targeted regularization techniques to mitigate variance among expert predictions, facilitating a smooth transition from an exclusively additive model to one that captures intricate feature interactions while maintaining clarity in feature attributions. Our theoretical analysis and experiments on synthetic data illustrate the model's flexibility, and extensive evaluations on real-world datasets confirm that NAEs achieve an optimal balance between predictive accuracy and transparent, feature-level explanations. The code is available at https://github.com/Teddy-XiongGZ/NAE.

神经可加专家：基于情境门控的可控模型可加性专家系统

Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity

摘要

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