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Activity Number: 559 - Randomized Algorithms for Optimization Problems in Statistics
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #306366
Title: Random Projections for Faster Non-Convex Optimization
Author(s): Mert Pilanci*
Companies: Stanford University
Keywords: non-convex optimization; neural nets; random projection

Randomized dimension reduction has recently become a powerful tool in machine learning, numerical linear algebra and signal processing. We consider random projection methods in the context of non-convex optimization problems for machine learning and statistics. First, we introduce a statistical model where the maximum likelihood estimator reduces to fitting a single layer neural network. We prove that a second order optimization method with a suitable initialization recovers the global optimum under certain assumptions on the data. We then introduce random projection, sampling and distributed optimization strategies that enable solving large scale non-convex learning problems faster than existing methods.

Authors who are presenting talks have a * after their name.

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