[Lisa_teatalk] [Lisa_labo] Talk by Sungjin Ahn on 1 Apr at 14.00

Li Yao yaoli.email at gmail.com
Wed Apr 1 13:57:18 EDT 2015


T talk reminder! Happening Now!

On Sat, Mar 28, 2015 at 2:52 PM, Kyung Hyun Cho <cho.k.hyun at gmail.com>
wrote:

> Dear all,
>
> Sungjin Ahn of UC Irvine (http://www.ics.uci.edu/~sungjia/) will visit us
> this Wednesday (1 Apr). He is an expert in applying Bayesian approaches to
> large-scale machine learning problems (stochastic MCMC). He will tell us
> about "Scalable MCMC in the era of Big Data" at 14.00 in the usual place
> (AA3195).
>
> If you're interested in talking to Sungjin, please, email me and Sungjin.
>
> Hope to see many of you on Wednesday!
> - Cho
>
> ===
> Speaker: Sungjin Ahn, UC Irvine
> Date/Time: 1 April @14.00
> Place: AA3195
>
> Title: Scalable MCMC in the era of Big Data
>
> Abstract:
> Scaling up Markov chain Monte Carlo (MCMC) methods for the web-scale data
> is one of the most important issues in probabilistic inference in this era.
> Whereas the most efficient traditional MCMC algorithms, e.g. HMC, have
> failed to adapt to this scale since it requires to evaluate all data points
> per iteration, the stochastic gradient methods in optimization have
> successfully shown its efficiency. In this talk, I introduce a family of
> MCMC algorithms based the Stochastic Gradient Langevin Dynamics, and its
> extension for distributed computation. The algorithms bring down the
> computation of MCMC to the same level as the stochastic gradient
> optimization by making it possible for the transition kernel to be based
> only on mini-batches and Gaussian noises. Also, several successful results
> in application to the most interesting large-scale problems such as topic
> modeling, matrix factorization, and neural networks, are presented.
>
> Bio:
> Sungjin Ahn is a Ph.D. candidate in the department of Computer Science at
> the University of California, Irvine. Prior to UC Irvine, he received his
> M.Sc. at the Korea Advanced Institute of Science and Technology (KAIST) and
> then worked at the Agency for Defense Development as a research scientist.
> During his PhD, he has worked on scalable Bayesian inference methods, in
> particular focusing on the stochastic gradient MCMC methods. Along with
> Bayesian inference methods for machine learning, his research interest also
> includes developing efficient computational models and learning procedures
> for vision and natural language information.
>
>
>
>
>
>
> _______________________________________________
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>
>
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