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Sparse Approximations
César Lincoln Mattos

Mon Dec 06 11:15 AM -- 11:37 AM (PST) @

Standard GP presents scalability issues, thus, in this section we present how sparse approximations enable the use of GPs for large datasets. We consider, in particular, the variational approach based on inducing variables, which results in tractable approximations for performing predictions and model selection via optimization of the evidence lower bound (ELBO).

Author Information

César Lincoln Mattos (Federal University of Ceará)

César Lincoln Cavalcante Mattos is an associate professor at the Department of Computer Science, at Federal University of Ceará (UFC), Brazil. He is also an associate researcher at the Logics and Artificial Intelligence Group (LOGIA). He has research interests in the broad fields of machine learning and probabilistic modeling, such as Gaussian processes, deep (probabilistic) learning, approximate inference and system identification. He has been applying learning methods in several research and development collaborations in areas such as dynamical system modeling, health risk analysis, software repository mining and anomaly detection.

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