Universal Algorithm-Implicit Learning

Abstract

Meta-learning methods are typically constrained to narrow task distributions with fixed feature and label spaces. We introduce a theoretical framework that formally defines practical universality and present TAIL, a transformer-based meta-learner that works across varying domains, modalities, and label configurations. TAIL achieves state-of-the-art on few-shot benchmarks while generalising to unseen domains and modalities.