Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data are often complex and pose several unique challenges for machine learning models: 1) multiple models are needed to handle region-based data patterns that have significant spatial heterogeneity across different locations; 2) local models trained on region-specific data have limited ability to adapt to other regions that have large diversity and abnormality; 3) spatial and temporal variations entangle data complexity that requires more robust and adaptive models; 4) limited spatial-temporal data in real scenarios (e.g., crop yield data is collected only once a year) makes the problems intrinsically challenging. To bridge these gaps, we propose task-adaptive formulations and a model-agnostic meta-learning framework that ensembles regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.