[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Eric Xing: Thrus, Oct 6th- 4:30pm

David Blei blei at CS.Princeton.EDU
Mon Oct 3 21:36:12 EDT 2011

hi ml-stat-talks,

eric xing speaks this thursday october 6 at 4:30 in the CS department.
 (come at 4PM for tea and cookies.)  he is an expert in graphical
models, especially in their application to computational biology,
social network analysis, and text.


---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Fri, Sep 30, 2011 at 11:24 AM
Subject: [talks] Colloquium Speaker Eric Xing: Thrus, Oct 6th- 4:30pm
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>

Jointly Maximum Margin and Maximum Entropy Learning of Graphical Models
Eric Xing, Carnegie Mellon University
Thursday, October 6, 2011- 4:30pm
Computer Science, 105

Graphical models (GMs) offer a powerful language to elegantly define
expressive distributions, and a generic computational framework to
support reasoning under uncertainty in a wide range of problems.
Popular paradigms for training GMs include the maximum likelihood
estimation, and more recently the max-margin learning, each enjoys
some advantages, as well as weaknesses. For example, the maximum
margin structured prediction model such as M3N lacks a straightforward
probabilistic interpretation of the learning scheme and the prediction
rule. Therefore its unique advantages such as support vector sparsity
and kernel tricks cannot be easily conjoined with the merits of a
probabilistic model such as Bayesian regularization, model averaging,
and ability to model hidden variables.

In this talk, I present a new general framework called Maximum Entropy
Discrimination Markov Networks (MEDN), which integrates the
margin-based and likelihood-based approaches and combines and extends
their merits. This new learning paradigm naturally facilitates
integration of the generative and discriminative principles under a
unified framework, and the basic strategies can be generalized to
learn arbitrary GMs, such as the generative Bayesian networks, models
with structured hidden variables, and even nonparametric Bayesian
models, with a desirable maximum margin effect on structured or
unstructured predictions. I will discuss a number of theoretical
properties of this approach, and show applications of MEDN to learning
a wide range of GMs including: fully supervised structured i/o model,
max-margin structured i/o models with hidden variables, a max-margin
LDA-style model for jointly discovering “discriminative” latent topics
and predicting document label/score of text documents, or total scene
and objective categories in natural images, etc. Our empirical results
strongly suggest that, for any GM with structured or unstructured
labels, MEDN always leads to a more accurate predictive GM than the
one trained under either MLE or Max Margin.

Joint work with Jun Zhu.

Dr. Eric Xing is an associate professor in the School of Computer
Science at Carnegie Mellon University. His principal research
interests lie in the development of machine learning and statistical
methodology; especially for solving problems involving automated
learning, reasoning, and decision-making in high-dimensional and
dynamic possible worlds; and for building quantitative models and
predictive understandings of biological systems. Professor Xing
received a Ph.D. in Molecular Biology from Rutgers University, and
another Ph.D. in Computer Science from UC Berkeley. His current work
involves, 1) foundations of statistical learning, including theory and
algorithms for estimating time/space varying-coefficient models,
sparse structured input/output models, and nonparametric Bayesian
models; 2) computational and statistical analysis of gene regulation,
genetic variation, and disease associations; and 3) application of
statistical learning in social networks, computer vision, and natural
language processing. Professor Xing has published over 140
peer-reviewed papers, and is an associate editor of the Annals of
Applied Statistics, the PLoS Journal of Computational Biology, and an
Action Editor of the Machine Learning journal. He is a recipient of
the NSF Career Award, the Alfred P. Sloan Research Fellowship in
Computer Science, and the United States Air Force Young Investigator

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