Le Song
Tuesday 21st October 2014
Time: 4pm
Basement Seminar Room
Alexandra House, 17 Queen Square, London, WC1N 3AR
Scalable Kernel Embedding of Latent Variable Models
Kernel embedding of distributions maps distributions to the reproducing kernel Hilbert space (RKHS) of a kernel function, such that subsequent manipulations of distributions can be achieved via RKHS distances, linear and multilinear transformations, and spectral analysis. This framework has led to simple and effective nonparametric algorithms in various machine learning problems, such as feature selection, two-sample test, time-series modeling and belief propagation. In this talk, I will focus on kernel embedding of latent variable models where the components in the models can have nonparametric form. The presence of latent variables in a model induces a sophisticated low rank structure in its kernel embedding, and is exploited for designing kernel algorithms for learning the latent parameters. While the method can adapt to the increasing complexity of the data as their volume grow, it is not scalable to large datasets. I will also introduce an approach called doubly stochastic functional gradients to scale up the methods and present some empirical results.
Short Bio:
Le Song is an assistant professor in the Department of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. He received his Ph.D. in Computer Science from University of Sydney in 2008, and then conducted his post-doctoral research in the School of Computer Science, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology, he worked briefly as a research scientist at Google. His principal research interests lie in nonparametric and kernel methods, probabilistic graphical models, spatial/temporal dynamics of networked processes, and the applications of machine learning to interdisciplinary problems. He is the recipient of NSF CAREER Award 2014, NIPS’13
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