Bayesian Alignments of Warped Multi-Output Gaussian Processes. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (NeurIPS, 2018)

Multimodal Deep Gaussian Processes. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (CoRR, 2018)

interpretable dynamics models for data-efficient reinforcement learning. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (ESANN, 2019)

Data Association with Gaussian Processes. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (ECML/PKDD (2), 2019)

Modulated Bayesian Optimization using Latent Gaussian Process Models. - Erik Bodin; Markus Kaiser 001; Ieva Kazlauskaite; Neill D. F. Campbell; Carl Henrik Ek (CoRR, 2019)

Modulating Surrogates for Bayesian Optimization. - Erik Bodin; Markus Kaiser 001; Ieva Kazlauskaite; Zhenwen Dai; Neill W. Campbell; Carl Henrik Ek (ICML, 2020)

Compositional uncertainty in deep Gaussian processes. - Ivan Ustyuzhaninov; Ieva Kazlauskaite; Markus Kaiser 001; Erik Bodin; Neill D. F. Campbell; Carl Henrik Ek (UAI, 2020)

Bayesian decomposition of multi-modal dynamical systems for reinforcement learning. - Markus Kaiser 001; Clemens Otte; Thomas A. Runkler; Carl Henrik Ek (Neurocomputing, 2020)

Structured Models with Gaussian Processes. - Markus Kaiser 001 (2021)

Ice Core Dating using Probabilistic Programming. - Aditya Ravuri; Tom R. Andersson; Ieva Kazlauskaite; Will Tebbutt; Richard E. Turner; J. Scott Hosking; Neil D. Lawrence; Markus Kaiser 001 (CoRR, 2022)

Optimisation of a global climate model ensemble for prediction of extreme heat days. - Mala Virdee; Markus Kaiser 001; Emily Shuckburgh; Carl Henrik Ek; Ieva Kazlauskaite (CoRR, 2022)