Fast Gaussian process learning for regression, semi-supervised classification, and multiway analysis

Add to your list(s) Download to your calendar using vCal

• Prof Alan Qi (Purdue U)
• Wednesday 04 July 2012, 14:00-15:00
• Engineering Department, CBL Room BE-438.

If you have a question about this talk, please contact Zoubin Ghahramani.

In this talk I will cover two topics on Gaussian process learning. First, I will describe tensor-variate latent nonparametric Bayesian models, coupled with efficient inference methods, for multiway data analysis. Unlike classical tensor decomposition models, our new approaches model nonlinear interactions and handle both continuous and binary data. To efficiently learn the InfTucker from data, we develop a variational inference technique on tensors. Compared with classical implementation, the new technique reduces both time and space complexities by several orders of magnitude. Our experimental results on chemometrics and social network datasets demonstrate that our new models can achieve significantly higher prediction accuracy than state-of-art tensor decomposition approaches. Furthermore, for two dimensional problems, our tensor model reduces to nonlinear stochastic blockmodels for network modeling, which I will briefly discuss in the talk as well. Second, I will describe a new sparse Gaussian process model, EigenGP, based on Karhunen-Loeve (KL) expansions of a GP prior. We can view this new approach as sparse PCA in a functional space, which not only reduces the computational cost of GP inference but also has the potential of further improving the predictive performance of a full GP. By selecting eigenfunctions of Gaussian kernels that are associated with data clusters, EigenGP is also suitable for semi-supervised learning. Our experimental results demonstrate improved predictive performance of EigenGP over several state-of-the- art sparse GP and semisupervised learning methods for regression, classification, and semisupervised classification.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

• Seminar
• All Talks (aka the CURE list)
• Biology
• CBL important
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge Neuroscience Seminars
• Cambridge talks
• Chris Davis' list
• Creating transparent intact animal organs for high-resolution 3D deep-tissue imaging
• Engineering Department, CBL Room BE-438
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• Joint Machine Learning Seminars
• Life Science
• Life Sciences
• ML
• Machine Learning @ CUED
• Machine Learning Summary
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Required lists for MLG
• Simon Baker's List
• Stem Cells & Regenerative Medicine
• Trust & Technology Initiative - interesting events
• bld31
• dh539
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.