Machine learning methods can be a valuable aid in the scientific process, but they need to face challenging settings where data come from inhomogeneous experimental conditions. Recent meta-learning methods have made significant progress in multi-task learning, but they rely on black-box neural networks, resulting in high computational costs and limited interpretability. Leveraging the structure of the learning problem, we argue that multi-environment generalization can be achieved using a simpler learning model, with an affine structure with respect to the learning task. Crucially, we prove that this architecture can identify the physical parameters of the system, enabling interpreable learning. We demonstrate the competitive generalization performance and the low computational cost of our method by comparing it to state-of-the-art algorithms on physical systems, ranging from toy models to complex, non-analytical systems. The interpretability of our method is illustrated with original applications to physical-parameter-induced adaptation and to adaptive control.

翻译：机器学习方法可在科学过程中提供宝贵助力，但需应对数据来自异质实验条件的挑战性场景。近期元学习方法在多任务学习中取得显著进展，然而这些方法依赖黑箱神经网络，导致计算成本高昂且可解释性有限。利用学习问题的结构特性，我们论证了采用更简单的学习模型——即具有与学习任务相关的仿射结构——即可实现多环境泛化。关键之处在于，我们证明了该架构能够识别系统的物理参数，从而实现可解释学习。通过将本方法与涵盖玩具模型到复杂非解析物理系统的各类前沿算法进行对比，我们展示了其优异的泛化性能与极低计算成本。基于物理参数诱导的自适应控制等原创应用案例，进一步阐释了本方法的可解释性。