no code implementations • 26 Nov 2022 • Mala Virdee, Markus Kaiser, Emily Shuckburgh, Carl Henrik Ek, Ieva Kazlauskaite
Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble.
1 code implementation • 29 Oct 2022 • Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
Ice cores record crucial information about past climate.
1 code implementation • 17 Sep 2019 • Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.
no code implementations • 10 Jul 2019 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
In this paper, we present a Bayesian view on model-based reinforcement learning.
Model-based Reinforcement Learning reinforcement-learning +2
no code implementations • ICML 2020 • Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.
no code implementations • 16 Oct 2018 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significant noise, or exhibit multimodality.
no code implementations • NeurIPS 2018 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field.