Statistical Insight into Meta-Learning via Predictor Subspace Characterization and Quantification of Task Diversity
This provides a statistical framework for analyzing meta-learning, which is incremental as it builds on existing paradigms to offer insights into performance factors.
The paper tackles the problem of understanding meta-learning performance by analyzing predictor subspace alignment and task diversity, showing that prediction accuracy depends on the proportion of predictor variance in the shared subspace and the accuracy of subspace estimation.
Meta-learning has emerged as a powerful paradigm for leveraging information across related tasks to improve predictive performance on new tasks. In this paper, we propose a statistical framework for analyzing meta-learning through the lens of predictor subspace characterization and quantification of task diversity. Specifically, we model the shared structure across tasks using a latent subspace and introduce a measure of diversity that captures heterogeneity across task-specific predictors. We provide both simulation-based and theoretical evidence indicating that achieving the desired prediction accuracy in meta-learning depends on the proportion of predictor variance aligned with the shared subspace, as well as on the accuracy of subspace estimation.