Sparse gaussian processes for large-scale machine learning

Resumo

Gaussian Processes (GPs) are non-parametric, Bayesian models able to achieve state-of-the-art performance in supervised learning tasks such as non-linear regression and classification, thus being used as building blocks for more sophisticated machine learning applications. GPs also enjoy a number of other desirable properties: They are virtually overfitting-free, have sound and convenient model selection procedures, and provide so-called “error bars”, i.e., estimations of their predictions’ uncertainty. Unfortunately, full GPs cannot be directly applied to real-world, large-scale data sets due to their high computational cost. For n data samples, training a GP requires O(n3) computation time, which renders modern desktop computers unable to handle databases with more than a few thousand instances. Several sparse approximations that scale linearly with the number of data samples have been recently proposed, with the Sparse Pseudo-inputs GP (SPGP) representing the current state of the art. Sparse GP approximations can be used to deal with large databases, but, of course, do not usually achieve the performance of full GPs. In this thesis we present several novel sparse GP models that compare favorably with SPGP, both in terms of predictive performance and error bar quality. Our models converge to the full GP under some conditions, but our goal is not so much to faithfully approximate full GPs as it is to develop useful models that provide high-quality probabilistic predictions. By doing so, even full GPs are occasionally outperformed. We provide two broad classes of models: Marginalized Networks (MNs) and Inter- Domain GPs (IDGPs). MNs can be seen as models that lie in between classical Neural Networks (NNs) and full GPs, trying to combine the advantages of both. Though trained differently, when used for prediction they retain the structure of classical NNs, so they can be interpreted as a novel way to train a classical NN, while adding the benefit of input-dependent error bars and overfitting resistance. IDGPs generalize SPGP by allowing the “pseudo-inputs” to lie in a different domain, thus adding extra flexibility and performance. Furthermore, they provide a convenient probabilistic framework in which previous sparse methods can be more easily understood. All the proposed algorithms are tested and compared with the current state of the art on several standard, large-scale data sets with different properties Their strengths and weaknesses are also discussed and compared, so that it is easier to select the best suited candidate for each potential application.