Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

Speaker: Mauricio Alvarez, Department of Computer Science, University of Sheffield
Abstract: Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voice recordings of multiple persons, each labeled with an ID. How could we build a model that captures the latent information related to these conditions and generalize to a new one with few data? We present a new model called Latent Variable Multiple Output Gaussian Processes (LVMOGP) that allows to jointly model multiple conditions for regression and generalize to a new condition with a few data points at test time. LVMOGP infers the posteriors of Gaussian processes together with a latent space representing the information about different conditions. We derive an efficient variational inference method for LVMOGP, for which the computational complexity is as low as sparse Gaussian processes. We show that LVMOGP significantly outperforms related Gaussian process methods on various tasks with both synthetic and real data.
Short Bio:Dr. Álvarez received a degree in Electronics Engineering (B. Eng.) with Honours, from Universidad Nacional de Colombia in 2004, a master degree in Electrical Engineering (M. Eng.) from Universidad Tecnológica de Pereira, Colombia in 2006, and a Ph.D. degree in Computer Science from The University of Manchester, UK, in 2011. After finishing his Ph.D., Dr. Álvarez joined the Department of Electrical Engineering at Universidad Tecnológica de Pereira, Colombia, where he was appointed as a Faculty member until Dec 2016. From January 2017, Dr. Álvarez was appointed as Lecturer in Machine Learning at the Department of Computer Science of the University of Sheffield, UK.
Dr. Álvarez is interested in machine learning in general, its interplay with mathematics and statistics, and its applications. In particular, his research interests include probabilistic models, kernel methods, and stochastic processes. He works on the development of new approaches and the application of Machine Learning in areas that include applied neuroscience, systems biology, and humanoid robotics