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Representation Learning with Gaussian Processes
Dr Amanda Muyskens: MuyGPs: Scalable Gaussian Process Model Estimation w/Uncertainty Quantification
Gaussian Process Emulators for Cardiac Digital Twins: Enabling Scalable Patient-Specific Modeling
#104 Automated Gaussian Processes & Sequential Monte Carlo, with Feras Saad
Simo Särkkä: State Space Representation of Gaussian Process
ML Tutorial: Gaussian Processes (Richard Turner)
Marcus Noack - Gaussian Process Approximation & Uncertainty Quantification for Autonomous Experiment
Practical and Scalable Inference for Deep Gaussian Processes, Maurizio Fillippone, bayesgroup.ru
Learning what we know and knowing what we learn: Gaussian process priors for neural data analysis
Georgios Giannakis - Ensemble Gaussian Processes for Online Learning with Scalability and Robustness
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Last Updated: October 18, 2026
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Summary
The third lecture of the Master class on Numerics of Machine Learning at the University of Tübingen in the Winter Term of 2022/23. My Intuitive Bayes Online Courses: intuitivebayes.com/ 1:1 Mentorship with me: topmate.io/alex_andorra This is ... ... the very interesting lectures and the first lecture is going to be on ... second day we're gonna have a session on Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! pymc-labs.com/ My Intuitive ... Machine Learning Tutorial at Imperial College London: Course: github.com/rmcelreath/stat_rethinking_2023 Intro music: youtube.com/watch?v=_3XGEsDSInM Outline ... The study of complex phenomena through the analysis of data often requires us to make assumptions about the underlying ... ... resources Rasmussen & Williams (2006) gaussianprocess.org/gpml/ The standard textbook for Prof. Georgios B. Giannakis, ADC Chair in Wireless Telecommunications and McKnight Presidential Chair in ECE, University of ...