| Data association with Gaussian processes M Kaiser, C Otte, TA Runkler, CH Ek Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 28* | 2019 |
| 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 | 24 | 2020 |
| Bayesian alignments of warped multi-output Gaussian processes M Kaiser, C Otte, T Runkler, CH Ek Advances in Neural Information Processing Systems 31, 2018 | 24 | 2018 |
| 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 | 17 | 2020 |
| Interpretable dynamics models for data-efficient reinforcement learning M Kaiser, C Otte, T Runkler, CH Ek arXiv preprint arXiv:1907.04902, 2019 | 10 | 2019 |
| Bayesian decomposition of multi-modal dynamical systems for reinforcement learning M Kaiser, C Otte, TA Runkler, CH Ek Neurocomputing 416, 352-359, 2020 | 9 | 2020 |
| 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 | 5 | 2023 |
| 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 | 4 | 2023 |
| 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, ... | 1 | 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 | 1 | 2023 |
| Structured Models with Gaussian Processes M Kaiser Technische Universität München, 2021 | 1 | 2021 |
| Modulated Bayesian Optimization using Latent Gaussian Process Models E Bodin, M Kaiser, I Kazlauskaite, NDF Campbell, CH Ek stat 1050, 26, 2019 | 1 | 2019 |
| Incorporating Uncertainty into Reinforcement Learning through Gaussian Processes M Kaiser Master’s Thesis. Munich: Technical University of Munich, 15, 2016 | 1 | 2016 |
| Calculations of extreme sea level rise scenarios are strongly dependent on ice sheet model resolution CR Williams, P Thodoroff, RJ Arthern, J Byrne, JS Hosking, M Kaiser, ... Communications Earth & Environment 6 (1), 60, 2025 | | 2025 |
| Method and system for controlling a production system M Kaiser, K Heesche, S Depeweg US Patent App. 18/293,859, 2024 | | 2024 |
| Method and system for controlling a production system S Depeweg, K Heesche, M Kaiser US Patent App. 18/562,370, 2024 | | 2024 |
| Method for controlling a gas turbine by means of a future combustion dynamic M Kaiser, K Heesche US Patent 11,898,501, 2024 | | 2024 |
| 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 |
Carl Henrik EkUniversity of CambridgeVerified email at cam.ac.uk