Rethinking Meta-Learning from a Learning Lens

2024-09-13

arXiv.org

AI Summary

Meta-learning models often suffer from overfitting to training tasks and poor generalization, stemming from task-wise co-adaptation that induces dual risks—both overfitting and underfitting. Method: This work systematically analyzes error sources from a learning dynamics perspective and proposes a task-relation-driven calibration paradigm: (i) constructing a task relationship matrix; (ii) designing relation-aware consistency regularization; (iii) introducing meta-data-driven task similarity estimation; and (iv) conducting theory-guided optimization stability analysis. Based on this, we develop TRLearner—a plug-and-play method requiring no architectural or data modifications. Contribution/Results: TRLearner significantly improves generalization across multiple benchmarks. Theoretically, it ensures enhanced convergence guarantees; empirically, stronger task similarity yields more pronounced collaborative gains, validating the efficacy of relation-aware calibration.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Meta-learning has emerged as a powerful approach for leveraging knowledge from previous tasks to solve new tasks. The mainstream methods focus on training a well-generalized model initialization, which is then adapted to different tasks with limited data and updates. However, it pushes the model overfitting on the training tasks. Previous methods mainly attributed this to the lack of data and used augmentations to address this issue, but they were limited by sufficient training and effective augmentation strategies. In this work, we focus on the more fundamental learning to learn strategy of meta-learning to explore what causes errors and how to eliminate these errors without changing the environment. Specifically, we first rethink the algorithmic procedure of meta-learning from a learning lens. Through theoretical and empirical analyses, we find that (i) this paradigm faces the risk of both overfitting and underfitting and (ii) the model adapted to different tasks promote each other where the effect is stronger if the tasks are more similar. Based on this insight, we propose using task relations to calibrate the optimization process of meta-learning and propose a plug-and-play method called Task Relation Learner (TRLearner) to achieve this goal. Specifically, it first obtains task relation matrices from the extracted task-specific meta-data. Then, it uses the obtained matrices with relation-aware consistency regularization to guide optimization. Extensive theoretical and empirical analyses demonstrate the effectiveness of TRLearner.

Problem

• Meta-learning overfits on training tasks.
• Task similarity affects model adaptation effectiveness.
• Task relations optimize meta-learning process.

Innovation

• Task Relation Learner (TRLearner)
• relation-aware consistency regularization
• calibration of meta-learning optimization