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The Art of Gaussian Processes: Classical and Contemporary
César Lincoln Mattos · Felipe Tobar

Mon Dec 06 09:00 AM -- 01:00 PM (PST) @ Virtual
Event URL: https://github.com/GAMES-UChile/The_Art_of_Gaussian_Processes »

Gaussian processes (GP) are Bayesian nonparametric models for continuous functions which allow for uncertainty quantification, interpretability, and the incorporation of expert knowledge. The theory and practice of GPs have flourished in the last decade, where researchers have looked into the expressiveness and efficiency of GP-based models and practitioners have applied them to a plethora of disciplines. This tutorial presents both the foundational theory and modern developments of data modelling using GPs, following step by step intuitions, illustrations and real-world examples. The tutorial will start with emphasis on the building blocks of the GP model, to then move onto the choice of the kernel function, cost-effective training strategies and non-Gaussian extensions. The second part of the tutorial will showcase more recent advances, such as latent variable models, deep GPs, current trends on kernel design and connections between GPs and deep neural networks. We hope that this exhibition, featuring classic and contemporary GP works, inspires attendees to incorporate GPs into their applications and motivates them to continue learning and contributing to the current developments in the field.

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.

Felipe Tobar (Universidad de Chile)

Felipe Tobar is an Assistant Professor at the Data & AI Initiative at Universidad de Chile. He holds Researcher positions at the Center for Mathematical Modeling and the Advanced Center for Electrical Engineering. Felipe received the BSc/MSc degrees in Electrical Engineering (U. de Chile, 2010) and a PhD in Signal Processing (Imperial College London, 2014), and he was an Associate Researcher in Machine Learning at the University of Cambridge (2014-2015). Felipe teaches Statistics and Machine Learning courses at undergraduate, graduate and professional levels. His research interests lie in the interface between Machine Learning and Statistical Signal Processing, including Gaussian processes, spectral estimation, approximate inference, Bayesian nonparametrics, and optimal transport.

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