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State Space Gaussian Processes with Non-Gaussian Likelihood
Hannes Nickisch · Arno Solin · Alexander Grigorevskiy

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #151

We provide a comprehensive overview and tooling for GP modelling with non-Gaussian likelihoods using state space methods. The state space formulation allows for solving one-dimensonal GP models in O(n) time and memory complexity. While existing literature has focused on the connection between GP regression and state space methods, the computational primitives allowing for inference using general likelihoods in combination with the Laplace approximation (LA), variational Bayes (VB), and assumed density filtering (ADF) / expectation propagation (EP) schemes has been largely overlooked. We present means of combining the efficient O(n) state space methodology with existing inference methods. We also furher extend existing methods, and provide unifying code implementing all approaches.

Author Information

Hannes Nickisch (Philips Research)
Arno Solin (Aalto University)
Arno Solin

Dr. Arno Solin is Assistant Professor in Machine Learning at the Department of Computer Science, Aalto University, Finland, and Adjunct Professor (Docent) at Tampere University, Finland. His research focuses on probabilistic models combining statistical machine learning and signal processing with applications in sensor fusion, robotics, computer vision, and online decision making. He has published around 50 peer-reviewed articles and one book. Previously, he has been a visiting researcher at Uppsala University (2019), University of Cambridge (2017-2018), and University of Sheffield (2014), and worked as a Team Lead in a tech startup. Prof. Solin is the winner of several prizes, hackathons, and modelling competitions, including the Schizophrenia Classification Challenge on Kaggle and the ISIF Jean-Pierre Le Cadre Best Paper Award. Homepage: http://arno.solin.fi

Alexander Grigorevskiy (Aalto University)

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