Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning

• URL: http://arxiv.org/abs/2102.07206v1
• Date: Sun, 14 Feb 2021 17:40:04 GMT
• Title: Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning
• Authors: Halil Ibrahim Gulluk, Yue Sun, Samet Oymak, Maryam Fazel
• Abstract summary: This work explores a more general class of nonlinear tasks with applications ranging from binary classification to neural nets. We prove that subspace-based representations can be learned in a sample-efficient manner and provably benefit future tasks in terms of sample complexity.
• Score: 28.2312127482203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Constructing good representations is critical for learning complex tasks in a sample efficient manner. In the context of meta-learning, representations can be constructed from common patterns of previously seen tasks so that a future task can be learned quickly. While recent works show the benefit of subspace-based representations, such results are limited to linear-regression tasks. This work explores a more general class of nonlinear tasks with applications ranging from binary classification, generalized linear models and neural nets. We prove that subspace-based representations can be learned in a sample-efficient manner and provably benefit future tasks in terms of sample complexity. Numerical results verify the theoretical predictions in classification and neural-network regression tasks.

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