ISyE Seminar - Feng Ruan

Event Details
  • Date/Time:
    • Tuesday February 12, 2019
      11:00 am - 12:00 pm
  • Location: ISyE Main Room 228
  • Phone:
  • URL: ISyE Building Complex
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
No contact information submitted.
Summaries

Summary Sentence: Adapting Maximum Likelihood Theory in Modern Applications

Full Summary: Abstract: Maximum likelihood estimation (MLE) is influential because it can be easily applied to generate optimal, statistically efficient procedures for broad classes of estimation problems. Nonetheless, the theory does not apply to modern settings—such as problems with computational, communication, or privacy considerations—where our estimators have resource constraints. In this talk, I will introduce a modern maximum likelihood theory that addresses these issues, focusing specifically on procedures that must be computationally efficient or privacy-preserving. To do so, I first derive analogues of Fisher information for these applications, which allows a precise characterization of tradeoffs between statistical efficiency, privacy, and computation. To complete the development, I also describe a recipe that generates optimal statistical procedures (analogues of the MLE) in the new settings, showing how to achieve the new Fisher information lower bounds.

Title:

Adapting Maximum Likelihood Theory in Modern Applications

Abstract:

Maximum likelihood estimation (MLE) is influential because it can be easily applied to generate optimal, statistically efficient procedures for broad classes of estimation problems. Nonetheless, the theory does not apply to modern settings—such as problems with computational, communication, or privacy considerations—where our estimators have resource constraints. In this talk, I will introduce a modern maximum likelihood theory that addresses these issues, focusing specifically on procedures that must be computationally efficient or privacy-preserving. To do so, I first derive analogues of Fisher information for these applications, which allows a precise characterization of tradeoffs between statistical efficiency, privacy, and computation. To complete the development, I also describe a recipe that generates optimal statistical procedures (analogues of the MLE) in the new settings, showing how to achieve the new Fisher information lower bounds.

Bio:

Feng Ruan is a fifth year Ph.D. student in the Department of Statistics at Stanford University, advised by Prof. John Duchi. He is broadly interested in developing theory and algorithm for inference under resource constraints, for stochastic convex and nonconvex optimization, and for high dimensional statistics. He is a recipient of the E.K. Potter Stanford Graduate Fellowship from Stanford University.

Additional Information

In Campus Calendar
Yes
Groups

H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

Invited Audience
Faculty/Staff, Postdoc, Public, Graduate students, Undergraduate students
Categories
Seminar/Lecture/Colloquium
Keywords
No keywords were submitted.
Status
  • Created By: sbryantturner3
  • Workflow Status: Published
  • Created On: Feb 5, 2019 - 11:08am
  • Last Updated: Feb 7, 2019 - 6:15pm