[Colloquium] TTIC Talks: Daniel Hsu, Microsoft Research New England

[Liv Leader]

REMINDER:

When:     Monday, April 16 @ 11 a.m.

Where:    TTIC, 6045 S. Kenwood Avenue, Room 526

Who:      Daniel Hsu, Microsoft Research New England

Title:       Algorithms for learning latent variable models

Latent variable models are widely used in applications to automatically
recover simple underlying signals from noisy high-dimensional data.  The
challenge in estimating such models stems from the presence of hidden
(unobserved) variables, and typical local search methods used for this task
(e.g., E-M) generally lack basic performance guarantees such as statistical
consistency and computational efficiency.  In this talk, I will discuss
recent developments in linear algebraic methods for learning certain
classes of latent variable models, including parameter estimation for
hidden Markov models, and structure learning of latent variable tree
models.  Unlike the local search heuristics, the proposed linear algebraic
methods come with statistical and computational efficiency guarantees under
mild conditions on the data distribution.  Central to the new techniques is
the characterization of the models in terms of low-order moments (e.g.,
averages, correlations) of observable variables, which are readily
estimated from data.

This talk is based on various joint works with Anima Anandkumar, Kamalika
Chaudhuri, Sham Kakade, Le Song, and Tong Zhang.

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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