Efficient Optimization for Sparse Gaussian Process Regression
This work addresses the computational bottleneck in Gaussian process regression for large datasets, though it is incremental as it builds on existing sparsity methods.
The authors tackled the problem of scaling Gaussian process regression by developing an efficient algorithm for selecting a sparse subset of training data, achieving state-of-the-art performance in discrete cases and competitive results in continuous cases.
We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression. The algorithm estimates an 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 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 discrete cases and competitive results in the continuous case.