Related papers: Learning-to-learn non-convex piecewise-Lipschitz functions

Learning-to-learn non-convex piecewise-Lipschitz functions

URL: http://arxiv.org/abs/2108.08770v1
Date: Thu, 19 Aug 2021 16:22:48 GMT
Title: Learning-to-learn non-convex piecewise-Lipschitz functions
Authors: Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar
Abstract summary: We analyze the meta-learning of the algorithms for piecewise-Lipschitz functions, a non-task with applications to both machine learning algorithms. We propose a practical meta-learning procedure that learns both the step-size of the algorithm from multiple online learning tasks.
Score: 44.6133187924678
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We analyze the meta-learning of the initialization and step-size of learning algorithms for piecewise-Lipschitz functions, a non-convex setting with applications to both machine learning and algorithms. Starting from recent regret bounds for the exponential forecaster on losses with dispersed discontinuities, we generalize them to be initialization-dependent and then use this result to propose a practical meta-learning procedure that learns both the initialization and the step-size of the algorithm from multiple online learning tasks. Asymptotically, we guarantee that the average regret across tasks scales with a natural notion of task-similarity that measures the amount of overlap between near-optimal regions of different tasks. Finally, we instantiate the method and its guarantee in two important settings: robust meta-learning and multi-task data-driven algorithm design.

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