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ORCIDMarkus Kaiser 0001
Person information
- affiliation (PhD 2021): Technical University of Munich, Germany
- affiliation: Siemens AG, Munich, Germany
Other persons with the same name
- Markus Kaiser 0002 — University of Koblenz-Landau, Software Languages Team, Germany
- Markus Kaiser 0003 — Siemens Healthcare, Advanced Therapies, Forchheim, Germany (and 2 more)
- Markus Kaiser 0004 — Darmstadt University of Technology, Germany
- Markus Kaiser 0005 — TH Nürnberg, Germany
- Markus Kaiser 0006 — University of the Bundeswehr, Institute of Flight Systems, Neubiberg, Germany
- Markus Kaiser 0007 — Hochschule Niederrhein University of Applied Sciences, Krefeld, Germany
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2020 – today
- 2022
[i7]Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence
, Markus Kaiser:
Ice Core Dating using Probabilistic Programming. CoRR abs/2210.16568 (2022)- [i6]Mala Virdee, Markus Kaiser, Emily Shuckburgh, Carl Henrik Ek, Ieva Kazlauskaite:
Optimisation of a global climate model ensemble for prediction of extreme heat days. CoRR abs/2211.16367 (2022) - 2021
- [b1]Markus Kaiser:
Structured Models with Gaussian Processes. Technical University of Munich, Germany, 2021 - 2020
- [j1]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian decomposition of multi-modal dynamical systems for reinforcement learning. Neurocomputing 416: 352-359 (2020) - [c5]Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek:
Modulating Surrogates for Bayesian Optimization. ICML 2020: 970-979 - [c4]Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek:
Compositional uncertainty in deep Gaussian processes. UAI 2020: 480-489
2010 – 2019
- 2019
- [c3]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
interpretable dynamics models for data-efficient reinforcement learning. ESANN 2019 - [c2]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Data Association with Gaussian Processes. ECML/PKDD (2) 2019: 548-564 - [i5]Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Neill D. F. Campbell, Carl Henrik Ek:
Modulated Bayesian Optimization using Latent Gaussian Process Models. CoRR abs/1906.11152 (2019) - [i4]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning. CoRR abs/1907.04902 (2019) - [i3]Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek:
Compositional uncertainty in deep Gaussian processes. CoRR abs/1909.07698 (2019) - 2018
- [c1]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian Alignments of Warped Multi-Output Gaussian Processes. NeurIPS 2018: 6995-7004 - [i2]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Multimodal Deep Gaussian Processes. CoRR abs/1810.07158 (2018) - 2017
- [i1]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian Alignments of Warped Multi-Output Gaussian Processes. CoRR abs/1710.02766 (2017)
Coauthor Index
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