Visiting Lecture Series: Jacob Abernethy, “Learning, Optimization, and the Benefits of Noise”

Event Details
  • Date/Time:
    • Tuesday April 5, 2016
      11:00 am - 12:30 pm
  • Location: Klaus Advance Computing Building, RM 1116, 266 Ferst Drive, Atlanta GA 30332
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Elizabeth Ndongi


Summary Sentence: Visiting Lecture from Jacob Abernathy, assistant professor in the Electrical Engineering and Computer Science Department at the University of Michigan, Ann Arbor. 

Full Summary: No summary paragraph submitted.

  • Jacob Abernethy Jacob Abernethy

In essentially every real-world learning and estimation problem, whether we are dealing with Big Data or Little Data, we must always address the same problem: overfitting. One approach to avoid overfitting is to ensure that your learning algorithm is sufficiently noisy, for example by introducing additional randomness (noise) into the procedure. The idea that you should perturb your data appears in various forms across a number of different fields, including Optimization, Economics, and Statistics. 

In this talk, we will expand upon this idea further, bringing out surprising connections between the pricing of options, stochastic smoothing techniques, and choice models. We will finish by discussing a new result that connects a popular randomized optimization procedure, known as simulated annealing, with the deterministic technique known as “Path-following Interior Point Methods”. This establishes a very strong and surprising relationship between the seminal works of Lovasz & Vempala and Nesterov & Nemirovski.



Jacob Abernethy is an assistant professor in the Electrical Engineering and Computer Science Department at the University of Michigan, Ann Arbor. He received his Ph.D. in Computer Science from the University of California, Berkeley, and was a Simons postdoctoral fellow at the University of Pennsylvania. Abernethy's primary interest is in Machine Learning, and he likes discovering connections between Optimization, Statistics, and Economics.

Additional Information

In Campus Calendar

College of Computing, School of Computer Science

Invited Audience
Undergraduate students, Faculty/Staff, Public, Graduate students
Computer Science, machine learning, School of Computer Science
  • Created By: Devin Young
  • Workflow Status: Published
  • Created On: Mar 28, 2016 - 5:22am
  • Last Updated: Apr 13, 2017 - 5:16pm