T talk reminder! Happening Now!
On Sat, Mar 28, 2015 at 2:52 PM, Kyung Hyun Cho cho.k.hyun@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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