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Beyond Gaussian Likelihood
César Lincoln Mattos

Mon Dec 06 11:00 AM -- 11:15 AM (PST) @

We expand the applicability of GP models by introducing non-Gaussian likelihoods. We show why inference becomes intractable and how it can be approximated. Via the use of illustrations, we focus on the binary classification learning scenario, warping functions, and heteroscedastic models.

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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