Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization

Published: Sep 5, 2018
Abstract
Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning, and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling and hyper-parameter optimization. Existing solutions attempt to adaptively trade-off between global exploration and local exploitation, wherein the initial exploratory sample is critical to their...
Paper Details
Title
Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization
Published Date
Sep 5, 2018
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