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Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
Alexander Terenin · Elizaveta Semenova · Geoff Pleiss · Zi Wang

Fri Dec 02 07:00 AM -- 04:00 PM (PST) @ Room 387
Event URL: https://gp-seminar-series.github.io/neurips-2022/ »

In recent years, the growth of decision-making applications, where principled handling of uncertainty is of key concern, has led to increased interest in Bayesian techniques. By offering the capacity to assess and propagate uncertainty in a principled manner, Gaussian processes have become a key technique in areas such as Bayesian optimization, active learning, and probabilistic modeling of dynamical systems. In parallel, the need for uncertainty-aware modeling of quantities that vary over space and time has led to large-scale deployment of Gaussian processes, particularly in application areas such as epidemiology. In this workshop, we bring together researchers from different communities to share ideas and success stories. By showcasing key applied challenges, along with recent theoretical advances, we hope to foster connections and prompt fruitful discussion. We invite researchers to submit extended abstracts for contributed talks and posters.

Author Information

Alexander Terenin (University of Cambridge)
Alexander Terenin

Alexander Terenin is a Postdoctoral Research Associate at the University of Cambridge. He is interested in statistical machine learning, particularly in settings where the data is not fixed, but is gathered interactively by the learning machine. This leads naturally to Gaussian processes and data-efficient interactive decision-making systems such as Bayesian optimization, to areas such as multi-armed bandits and reinforcement learning, and to techniques for incorporating inductive biases and prior information such as symmetries into machine learning models.

Elizaveta Semenova (University of Oxford)
Geoff Pleiss (Columbia University)
Zi Wang (Google Brain)

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