Folks, Jun Zhu will be speaking tomorrow at 3pm. I am meeting with him from 12:00-1:45 and Anna Choromanska is meeting with him from 1:45 to 2:45. Can any of you please meet with him between 10am and noon or at 4:30pm?
Bayesian Inference with Max-margin Posterior Regularization
Jun Zhu
Tsinghua University
Abstract:
Existing Bayesian models, especially nonparametric Bayesian methods, rely heavily on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors can affect posterior distributions through Bayes' theorem, imposing posterior regularization is arguably more direct and in some cases can be more natural and easier. In this talk, I will present regularized Bayesian inference (RegBayes), a computational framework to perform posterior inference with a convex regularization on the desired post-data posterior distributions. When the convex regularization is induced from a linear operator on the posterior distributions, RegBayes can be solved with convex analysis theory. Furthermore, I will present some concrete examples, including MedLDA for learning discriminative topic representations and infinite latent support vector machines for learning discriminative latent features! for classification. All these models explore the large-margin idea in combination with a (nonparametric) Bayesian model for discovering predictive latent representations. I will discuss both variational and Monte Carlo methods for approximate inference.
Bio:
Dr. Jun Zhu is an associate professor in the Department of Computer Science and Technology at Tsinghua University. His principal research interests lie in the development of statistical machine learning methods for solving scientific and engineering problems arising from artificial and biological learning, reasoning, and decision-making in the high-dimensional and dynamic worlds. Prof. Zhu received his Ph.D. in Computer Science from Tsinghua University, and his advisor was Prof. Bo Zhang. He did post-doctoral research with Prof. Eric P. Xing in the Machine Learning Department at Carnegie Mellon University. His current work involves both the foundations of statistical learning, including theory and algorithms for probabilistic latent variable models, sparse learning in high dimensions, Bayesian nonparametrics, and large-margin learning; and the application of statistical learning in social network analysis, data mining, and! multi-media data analysis.
Bayesian Inference with Max-margin Posterior Regularization
Jun Zhu
Tsinghua University
Abstract:
Existing Bayesian models, especially nonparametric Bayesian methods, rely
heavily on specially conceived priors to incorporate domain knowledge for
discovering improved latent representations. While priors can affect
posterior distributions through Bayes' theorem, imposing posterior
regularization is arguably more direct and in some cases can be more
natural and easier. In this talk, I will present regularized Bayesian
inference (RegBayes), a computational framework to perform posterior
inference with a convex regularization on the desired post-data posterior
distributions. When the convex regularization is induced from a linear
operator on the posterior distributions, RegBayes can be solved with convex
analysis theory. Furthermore, I will present some concrete examples,
including MedLDA for learning discriminative topic representations and
infinite latent support vector machines for learning discriminative latent
features! for classification. All these models explore the large-margin
idea in combination with a (nonparametric) Bayesian model for discovering
predictive latent representations. I will discuss both variational and
Monte Carlo methods for approximate inference.
Bio:
Dr. Jun Zhu is an associate professor in the Department of Computer Science
and Technology at Tsinghua University. His principal research interests lie
in the development of statistical machine learning methods for solving
scientific and engineering problems arising from artificial and biological
learning, reasoning, and decision-making in the high-dimensional and
dynamic worlds. Prof. Zhu received his Ph.D. in Computer Science from
Tsinghua University, and his advisor was Prof. Bo Zhang. He did
post-doctoral research with Prof. Eric P. Xing in the Machine Learning
Department at Carnegie Mellon University. His current work involves both
the foundations of statistical learning, including theory and algorithms
for probabilistic latent variable models, sparse learning in high
dimensions, Bayesian nonparametrics, and large-margin learning; and the
application of statistical learning in social network analysis, data
mining, and! multi-media data analysis.
-- Anna Choromanska
aec2...@columbia.edu
achor...@gmail.com
Dept. of Electrical Engineering
Columbia University
DVMM Lab
708 Schapiro CEPSR
http://www.columbia.edu/~aec2163/
