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Found 7 papers, 2 papers with code
Bayesian Alignments of Warped Multi-Output Gaussian Processes
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.
Data Association with Gaussian Processes
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.
Modulating Surrogates for Bayesian Optimization
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.
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning
In this paper, we present a Bayesian view on model-based reinforcement learning.
Model-based Reinforcement Learning
reinforcement-learning
+2
Compositional uncertainty in deep Gaussian processes
Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate.
A locally time-invariant metric for climate model ensemble predictions of extreme risk
Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble.
