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Efficient Optimization for Sparse Gaussian Process Regression
Yanshuai Cao · Marcus Brubaker · David Fleet · Aaron Hertzmann

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

We propose an efficient discrete optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression. The algorithm estimates this inducing set and the hyperparameters using a single objective, either the marginal likelihood or a variational free energy. The space and time complexity are linear in the training set size, and the algorithm can be applied to large regression problems on discrete or continuous domains. Empirical evaluation shows state-of-art performance in the discrete case and competitive results in the continuous case.

Author Information

Yanshuai Cao (BorealisAI)
Marcus Brubaker (York University)
David Fleet (Google Research, Brain Team and University of Toronto)
Aaron Hertzmann (Adobe Research)

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