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SSC Seminar Series - Yuan (Alan) Qi

Friday, March 21, 2014, 02:00pm - 03:00pm


Title: Scalable Gaussian Process Inference for Big Data

Abstract: Gaussian process (GP) models are powerful Bayesian nonparametric models. However, it is computationally intensive; given big data, the high computational cost has become a bottleneck for GPs' applications. To address this issue, my group has developed a series of sparse GP inference algorithms. Today I will cover two recent works of ours. First, I will present a scalable GP learning method, EigenGP, which provides nonlinear predictions in a subspace spanned by GP eigenfunctions, automatically learned from data. EigenGP enjoys accurate prediction and uncertainty quantification with fast computation. I will demonstrate its effectiveness on regression, time series forecasting, and semi-supervised classification. Second, I will present GP models on graphs and tensors for predicting unknown interactions or elements. Compared with state-of-the-art methods on benchmark datasets, our method achieves a striking three-fold error reduction. On tensors with billions of elements--which were impossible for existing GP inference methods)---using a distributed online inference algorithm on a new hierarchical Bayesian model, our method achieves higher prediction accuracy with less time than the state-of-the-art alternative. 

Location : CBA 4.330