Statistical Insight into Meta-Learning via Predictor Subspace Characterization and Quantification of Task Diversity

In recent years, there has been significant interest in designing machine learning algorithms that enable robust and sample-efficient knowledge transfer across tasks to facilitate rapid and accurate estimation and prediction. Traditional machine learning methods have largely followed a single-task or "isolated learning" framework, where each task is learned independently, ignoring knowledge from prior tasks (Upadhyay et al., 2024). However, unlike such isolated approaches, human learning relies on prior experiences to accelerate new learning. Inspired by this, recent prominent "knowledge-transfer" approaches include meta-learning (Finn et al., 2017; Bouchattaoui, 2024), transfer learning (Zhu et al., 2023; Zhuang et al., 2020), multi-task learning (Crawshaw, 2020; Zhang and Yang, 2022), and lifelong learning (Liu, 2017), all of which aim to leverage shared structure across tasks to improve generalization and aim to replicate this human-like knowledge transfer. Meta-learning focuses on learning a learning algorithm that can quickly adapt to new tasks using limited data. Transfer learning reuses knowledge from related source tasks to improve performance on a target task with few labeled examples.