[Colloquium] Friday, January 10 - 10:30 am ~ MLS Series ~ Mina Karzand

[Annie Simmons]

University of Chicago and Toyota Technological Institute at Chicago
Machine Learning Seminar Series


Mina Karzand, Postdoc
UW-Madison

Friday, January 10 10:30 - 11:30 am
TTIC, Rm 526
(Remote broadcast at JCL Rm 390)

Title:
Focused Learning in Tree-Structured Graphical Models

Abstract:
This talk is about the predictive power of learned graphical models where we show that an incorrect or incomplete combinatorial structure (graph) can nevertheless yield accurate predictions. 

In particular, in the first half of the talk, I look into learning tree structured Ising models in which the learned model is used subsequently for prediction based on partial observations (given the realization of a subset of variables, predict the value of the remaining ones). The vast majority of previous work on learning graphical models aims to correctly recover the underlying graph structure.  In the data-constrained regime, learning the entire graph structure correctly is usually impossible. I show that it is possible to efficiently learn a tree model that gives accurate predictions even when there is insufficient data to learn the correct structure.

The second half of the talk is about speciation rate estimation in phylogenetic trees. This problem is essentially one of inferring features of
sequences at the leaves of the tree) of a latent tree model (phylogeny). I show that to estimate the speciation rate efficiently, it is not necessary to follow the popular approach of reconstructing the complete tree structure as an intermediate step, which requires long DNA sequences. Instead, one can extract precisely the right type of information about the rates by zooming into carefully chosen local structures. My results show that an incomplete and partially incorrect summary of the tree structure is enough to estimate the speciation rate with the minimax optimal dependence on the length of observed DNA sequences.
 
Joint work with Guy Bresler, Sebastien Roch and Robert Nowak.

Bio:
Mina Karzand is a postdoctoral associate in University of Wisconsin-Madison. Before that, she was a postdoctoral research associate in MIT where she received her PhD in Electrical Engineering and Computer Science. Her research interests are on the design and analysis of data driven inference and decision making systems in the intersection of areas of machine learning, probability, and information theory.
Host: Eric Jonas    








Annie Simmons
Project Assistant IV
Computer Science Department
John Crerar Library Building
5730 S. Ellis
Chicago, IL 60637 
773.834.2750
773.702.8487
simmons3 at cs.uchicago.edu

“The dream is free the hustle is sold separately"

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