[Colloquium] TTIC Talks: Piyush Rah, University of Utah

[Liv Leader]

REMINDER:

When:     Monday, January 30 @ 11 a.m.

Where:   TTIC Conference Room #530, 6045 S. Kenwood Avenue, 5th Floor

Who:      Piyush Rah, University of Utah

Title:      Flexible Bayesian Modeling of Latent Features and Shared
Predictive Structures

Abstract: Nonparametric Bayesian approaches offer a flexible modeling
paradigm for data without limiting the model-complexity a priori. In this
talk, I will describe some of my recent work on nonparametric Bayesian
learning of low-dimensional latent structures from high-dimensional data
prevalent in domains such as language, vision, and biology. In particular,
I will talk about (1) a nonparametric Bayesian sparse latent feature model
that allows the latent features to be related with each-other via an a
priori unknown hierarchy, (2) a nonparametric Bayesian multi-task learning
model that learns shared, latent predictive structures from multiple
learning tasks, and (3) an efficient, search-based inference method for
finding approximate MAP solution in nonparametric Bayesian latent feature
models.

Host: Nati Srebro, nati at ttic.edu

-- 
Liv Leader
Human Resources Coordinator

Toyota Technological Institute Chicago
6045 S Kenwood Ave
Chicago, IL 60637
Phone- (773) 702-5033
Fax-     (773) 834-9881
Email-  lleader at ttic.edu <jam at ttic.edu>
Web-   www.ttic.edu
<http://www.ttic.edu/>
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