From: "Lisa R. Lewis" <ll2@Princeton.EDU>

Subject: EE SEMINAR : Thursday September 18th, 4:30pm, E-Quad B205- Aryeh Kontorovich-Good Margins Make Good Neighbors

Date: September 10, 2014 at 10:29:06 AM EDT

To: ee-seminar@Princeton.EDU

Reply-To: "Lisa R. Lewis" <ll2@Princeton.EDU>

 

 

 

DEPARTMENT OF

ELECTRICAL ENGINEERING

SEMINAR SERIES

 

 

 

 

Speaker:      Dr. Aryeh Kontorovich

                        Ben-Gurion University of the Negev

Title:             Good Margins Make Good Neighbors

Date:             Thursday, September 18, 2014

Time:            4:30 p.m.  

Room:          E-Quad, B205

Host:            Prof. Sergio Verdu

 

 

Abstract:   Although well-known by practitioners to be an effective classification tool, nearest-neighbor methods have been somewhat neglected by learning theory of late. The goal of this talk is to revive interest in this time-tested technique by recasting it in a modern perspective. We will present a paradigm of margin-regularized 1-nearest neighbor classification which: (i) is Bayes-consistent (ii) yields simple, usable finite-sample error bounds (iii) provides for very efficient algorithms with a principled speed-accuracy tradeoff (iv) allows for near-optimal sample compression. Further extensions include multiclass, regression, and metric dimensionality reduction. I will argue that the regularized 1-nearest neighbor is superior to k-nearest neighbors in several crucial statistical and computational aspects.

Based on a series of works with: Lee-Ad Gottlieb, Robert Krauthgamer, Roi Weiss

 

 

Bio:  Aryeh Kontorovich received his undergraduate degree in mathematics with a certificate in applied mathematics from Princeton University in 2001. His M.Sc. and Ph.D. are from Carnegie Mellon University, where he graduated in 2007. After a postdoctoral fellowship at the Weizmann Institute of Science, he joined the Computer Science department at Ben-Gurion University of the Negev in 2009 as an assistant professor; this is his current position. His research interests are mainly in machine learning, with a focus on probability, statistics, automata theory and metric spaces.