In essence, the concept of “Gaussian Processes” (GP) exists for many years, but during the last few years they pop up in different disciplines, often presented in a new toolkit.
In December 2019 we launched an interdisciplinary interest group around the possibilities and the challenges of GP. The objective was (and still is) the organic expansion of a platform for discussions and sharing of ideas on theoretical contributions and practical applications, creating a fertile soil for future cooperation and project proposals.
You should care about this topic as soon as you get involved in topics like probabilistic numerics, interpolation and optimization in uncertain environments, explainable machine learning, design of sample measurements and pseudo-inputs, etc...
Lunch seminar on
Dynamic line scan thermography parameter design via Gaussian process emulation
by Simon Verspeek (UAntwerp)
Understanding the Significance, Processing and Analysis of Point Clouds
by Stuti Pathak (UAntwerp)
Bayesian Deep Learning with Physics-informed Gaussian Processes
by Thomas McDonald (University of Manchester)
Online seminar by Carl Henrik Ek (Cambridge University)
Online seminar by Inneke Van Nieuwenhuyse (UHasselt)
Date: 23/12/2021
Online seminar by Marcel Lüthi (University of Basel)
Date: 09/06/2021
Online seminar by Ivo Couckuyt (Ghent University)
Date: 23/02/2021
Lunch seminar by Timothy Verstraeten (Vrije Universiteit Brussel)
Date: 26/02/2020
Pilot lunch by Boris Bogaerts (UAntwerp) and Ivan De Boi (UAntwerp)
Date: 12/12/2019
De Boi, I.; Ek, C.H.; Penne, R. Surface Approximation by Means of Gaussian Process Latent Variable Models and Line Element Geometry.Mathematics2023,11, 380. https://doi.org/10.3390/math11020380
I. D. Boi, S. Sels, O. De Moor, S. Vanlanduit and R. Penne, "Input and Output Manifold Constrained Gaussian Process Regression for Galvanometric Setup Calibration," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8, 2022, Art no. 2509408, doi: 10.1109/TIM.2022.3170968.
Dutordoir, V., Hensman, J., van der Wilk, M., Ek, C. H., Ghahramani, Z., & Durrande, N. (2021). Deep neural networks as point estimates for deep Gaussian processes. Advances in Neural Information Processing Systems, 34.
https://nips.cc/Conferences/2021/ScheduleMultitrack?event=28276
Verspeek, S.; De Boi, I.; Maldague, X.; Penne, R.; Steenackers, G. Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation. Algorithms 2022, 15, 102. https://doi.org/10.3390/a15040102
https://www.mdpi.com/1999-4893/15/4/102
Kleijnen, J., van Nieuwenhuyse, I., & van Beers, W. C. M. (2021). Constrained Optimization in Simulation: Efficient Global Optimization and Karush-Kuhn-Tucker Conditions. (CentER Discussion Paper ; Vol. 2021-031). CentER, Center for Economic Research.
I. De Boi, S. Sels and R. Penne, "Semi Data-driven Calibration of Galvanometric Setups using Gaussian Processes," in IEEE Transactions on Instrumentation and Measurement, doi: 10.1109/TIM.2021.3128956.
https://ieeexplore.ieee.org/document/9618952
De Boi, I.; Ribbens, B.; Jorissen, P.; Penne, R. Feasibility of Kd-Trees in Gaussian Process Regression to Partition Test Points in High Resolution Input Space. Algorithms2020, 13, 327. https://www.mdpi.com/1999-4893/13/12/327
Verstraeten, T., Libin, P. & Nowé, A. (2020), Fleet Control using Coregionalized Gaussian Process Policy Iteration. In: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020). IOS Press, pp. 1571-1578, European Conference on Artificial Intelligence (ECAI 2020), Santiago De Compostela, Spain, 29/08/2020.
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