Structured Prediction for Conditional Meta-Learning

• URL: http://arxiv.org/abs/2002.08799v2
• Date: Mon, 19 Oct 2020 17:18:39 GMT
• Title: Structured Prediction for Conditional Meta-Learning
• Authors: Ruohan Wang, Yiannis Demiris, Carlo Ciliberto
• Abstract summary: We propose a new perspective on conditional meta-learning via structured prediction. We derive task-adaptive structured meta-learning (TASML), a principled framework that yields task-specific objective functions. Empirically, we show that TASML improves the performance of existing meta-learning models, and outperforms the state-of-the-art on benchmark datasets.
• Score: 44.30857707980074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of optimization-based meta-learning is to find a single initialization shared across a distribution of tasks to speed up the process of learning new tasks. Conditional meta-learning seeks task-specific initialization to better capture complex task distributions and improve performance. However, many existing conditional methods are difficult to generalize and lack theoretical guarantees. In this work, we propose a new perspective on conditional meta-learning via structured prediction. We derive task-adaptive structured meta-learning (TASML), a principled framework that yields task-specific objective functions by weighing meta-training data on target tasks. Our non-parametric approach is model-agnostic and can be combined with existing meta-learning methods to achieve conditioning. Empirically, we show that TASML improves the performance of existing meta-learning models, and outperforms the state-of-the-art on benchmark datasets.

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