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辅助，但需应对数据源自异质实验条件等严峻挑战。近期元学习方法在多任务学习领域取得显著进展，但这类方法依赖黑箱神经网络，导致计算成本高昂且可解释性受限。通过利用学习问题的结构特性，我们论证了可采用结构更简单（相对于学习任务具有仿射结构）的模型实现多环境泛化。关键突破在于，我们证明了该架构能识别系统的物理参数，从而实现可解释学习。通过将我们的方法与包括玩具模型到复杂非解析系统的各类物理系统上的最新算法进行对比，展示了其具备竞争力且计算成本低的泛化性能。通过物理参数诱导自适应与自适应控制等创新应用，阐释了我们方法的可解释性。