Predictive Deep Sets

Amortized meta-learning methods, such as neural processes, promise near-instantaneous inference on new labeled datasets encountered during downstream tasks. Recent adaptations of the transformer architecture have propelled these approaches to impressive performance in tasks like function estimation, parameter inference, and decision-making. Curiously, their success still primarily stems from the expressiveness of transformers, lacking a bias for modeling the functional structures between features and labels shared across datasets. We argue and show that this leads to training sample inefficiency and sub-optimal performance, and address this by introducing a novel set encoding technique called Predictive Deep Sets. Our approach exploits a strong bias for functional structures by meta-learning an RKHS that captures domain-critical functional patterns, and by representing datasets as optimal fit functions within this space. Besides providing theoretical justification for this approach, we empirically demonstrate orders of magnitude increases in training data sample efficiency compared to strong baselines across various settings.