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Markus Kaiser
Title
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Cited by
Year
Data association with Gaussian processes
M Kaiser, C Otte, TA Runkler, CH Ek
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019
25*2019
Bayesian alignments of warped multi-output Gaussian processes
M Kaiser, C Otte, T Runkler, CH Ek
Advances in Neural Information Processing Systems 31, 2018
242018
Compositional uncertainty in deep Gaussian processes
I Ustyuzhaninov, I Kazlauskaite, M Kaiser, E Bodin, N Campbell, CH Ek
Conference on Uncertainty in Artificial Intelligence, 480-489, 2020
212020
Modulating surrogates for Bayesian optimization
E Bodin, M Kaiser, I Kazlauskaite, Z Dai, N Campbell, CH Ek
International Conference on Machine Learning, 970-979, 2020
122020
Interpretable dynamics models for data-efficient reinforcement learning
M Kaiser, C Otte, T Runkler, CH Ek
arXiv preprint arXiv:1907.04902, 2019
122019
Bayesian decomposition of multi-modal dynamical systems for reinforcement learning
M Kaiser, C Otte, TA Runkler, CH Ek
Neurocomputing 416, 352-359, 2020
82020
Multi-fidelity experimental design for ice-sheet simulation
P Thodoroff, M Kaiser, R Williams, R Arthern, S Hosking, N Lawrence, ...
arXiv preprint arXiv:2307.08449, 2023
42023
Method and apparatus for cooperative controlling wind turbines of a wind farm
P Egedal, PB Enevoldsen, A Hentschel, M Kaiser, C Otte, V Sterzing, ...
US Patent 11,585,323, 2023
12023
Structured Models with Gaussian Processes
M Kaiser
Technische Universität München, 2021
12021
Modulated Bayesian Optimization using Latent Gaussian Process Models
E Bodin, M Kaiser, I Kazlauskaite, NDF Campbell, CH Ek
stat 1050, 26, 2019
12019
Multimodal Deep Gaussian Processes
M Kaiser, C Otte, T Runkler, CH Ek
arXiv preprint arXiv:1810.07158 16, 2018
12018
Incorporating Uncertainty into Reinforcement Learning through Gaussian Processes
M Kaiser
Master’s Thesis. Munich: Technical University of Munich, 15, 2016
12016
Calculating exposure to extreme sea level risk will require high resolution ice sheet models
C Williams, P Thodoroff, R Arthern, J Byrne, JS Hosking, M Kaiser, ...
2023
Determining future switching behavior of a system unit
M Tokic, S Depeweg, S Udluft, M Kaiser, D Hein
US Patent App. 17/909,044, 2023
2023
A locally time-invariant metric for climate model ensemble predictions of extreme risk
M Virdee, M Kaiser, CH Ek, E Shuckburgh, I Kazlauskaite
Environmental Data Science 2, e26, 2023
2023
A Metric to Evaluate Climate Models' Applicability for Extreme Event Prediction
M Virdee, CH Ek, M Kaiser, E Shuckburgh
AGU Fall Meeting Abstracts 2022, A22F-1732, 2022
2022
Probabilistic Machine Learning for Automated Ice Core Dating
A Ravuri, T Andersson, M Kaiser, I Kazlauskaite, M Fryer, JS Hosking, ...
AGU Fall Meeting Abstracts 2022, C52C-0361, 2022
2022
Optimisation of a global climate model ensemble for prediction of extreme heat days
M Virdee, E Shuckburgh, CH Ek, I Kazlauskaite, M Kaiser
arXiv preprint arXiv:2211.16367, 2022
2022
Ice Core Dating using Probabilistic Programming
A Ravuri, TR Andersson, I Kazlauskaite, W Tebbutt, RE Turner, ...
arXiv preprint arXiv:2210.16568, 2022
2022
Method and system for controlling a production plant to manufacture a product
K Heesche, S Depeweg, M Kaiser
US Patent App. 17/691,190, 2022
2022
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