ARC Colloquium: Surbhi Goel (Univ. of Texas at Austin)

Event Details
  • Date/Time:
    • Monday November 9, 2020
      11:00 am - 12:00 pm
  • Location: Virtual via Bluejeans
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Summaries

Summary Sentence: Computational Complexity of Learning Neural Networks over Gaussian Marginals: Virtual via Bluejeans at 11:00am

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Algorithms & Randomness Center (ARC)

Surbhi Goel (Univ. of Texas at Austin)

Monday, November 9, 2020

Virtual via Bluejeans - 11:00 am

 

Title: Computational Complexity of Learning Neural Networks over Gaussian Marginals

Abstract:  A major challenge in the theory of deep learning is to understand the computational complexity of learning basic families of neural networks (NNs). The challenge here arises from the non-convexity of the associated optimization problem. It is well known that the learning problem is computationally intractable in the worst case. Positive results have circumvented this hardness by making assumptions on the distribution as well as the label noise.

In this talk, we focus on the problem of learning shallow NNs under the benign gaussian input distribution. We first show a super-polynomial Statistical Query (SQ) lower bound in the noiseless setting. We further show how to use this result to obtain a super-polynomial SQ lower bound for learning a single neuron in the agnostic noise model. Lastly, on the positive side, we describe a gradient-based algorithm for approximately learning ReLUs which runs in polynomial time.

This talk is based on multiple works with Ilias Diakonikolas, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans and Mahdi Soltanolkotabi

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Videos of recent talks are available at: http://arc.gatech.edu/node/121

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ARC

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Faculty/Staff, Postdoc, Graduate students, Undergraduate students
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Seminar/Lecture/Colloquium
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Status
  • Created By: Francella Tonge
  • Workflow Status: Published
  • Created On: Sep 25, 2020 - 10:24am
  • Last Updated: Nov 2, 2020 - 11:25am