Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty. However, tracking uncertainty for iterative, multistep predictions in general leads to an analytically intractable problem. While approximation methods exist, they do not come with guarantees, making it difficult to estimate their reliability and to trust their predictions. In this work, we derive formal probability error bounds for iterative predictions with GPs. Building on GP properties, we bound the probability that random trajectories lie in specific regions around the predicted values. Namely, given a tolerance � > 0, we compute regions around the predicted trajectory values, such that GP trajectories are guarante...
Bayesian inference and Gaussian processes are widely used in applications ranging from robotics and ...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We fo...
Gaussian process [1] and it’s variants of deep structures like deep gaussian processes [2] and convo...
Gaussian Processes (GPs) are widely employed in control and learning because of their principled tre...
Gaussian Processes (GPs) are widely employed in control and learning because of their principled tre...
Gaussian Process regression is a popular nonparametric regression method based on Bayesian principle...
Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principle...
Due to the increasing complexity of technical systems, accurate first principle models can often not...
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation...
When learning continuous dynamical systems with Gaussian Processes, computing trajectories requires ...
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive ...
With the Gaussian Process model, the predictive distribution of the output corresponding to a new gi...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Established techniques for simulation and prediction with Gaussian process (GP) dynamics implicitly ...
Bayesian inference and Gaussian processes are widely used in applications ranging from robotics and ...
Bayesian inference and Gaussian processes are widely used in applications ranging from robotics and ...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We fo...
Gaussian process [1] and it’s variants of deep structures like deep gaussian processes [2] and convo...
Gaussian Processes (GPs) are widely employed in control and learning because of their principled tre...
Gaussian Processes (GPs) are widely employed in control and learning because of their principled tre...
Gaussian Process regression is a popular nonparametric regression method based on Bayesian principle...
Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principle...
Due to the increasing complexity of technical systems, accurate first principle models can often not...
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation...
When learning continuous dynamical systems with Gaussian Processes, computing trajectories requires ...
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive ...
With the Gaussian Process model, the predictive distribution of the output corresponding to a new gi...
Gaussian processes are attractive models for probabilistic classification but unfortunately exact in...
Established techniques for simulation and prediction with Gaussian process (GP) dynamics implicitly ...
Bayesian inference and Gaussian processes are widely used in applications ranging from robotics and ...
Bayesian inference and Gaussian processes are widely used in applications ranging from robotics and ...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We fo...
Gaussian process [1] and it’s variants of deep structures like deep gaussian processes [2] and convo...