Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes

Abstract

Finding efficient and provable methods to solve non-convex optimization problems is an outstanding challenge in machine learning. A popular approach used to tackle non-convex problems is to use convex relaxation techniques to find a convex surrogate for the problem. Unfortunately, convex relaxations typically must be found on a problemby-problem basis. Thus… (More)

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