Multiple-step ahead prediction for non linear dynamic systems: A Gaussian Process treatment with propagation of the uncertainty

By Agathe Girard, Carl E. Rasmussen, Joaquin Quiñonero-Candela, Roderick Murray-Smith, Becker, S. Thrun, Klaus Obermayer. 2003

83 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sergey Levine
2. Pieter Abbeel
3. Jan Peters
4. Marc Peter Deisenroth
5. Chelsea Finn
6. Danijar Hafner
7. Yoshua Bengio
8. Michael Janner

Citations in the corpus

• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018, methodology): “Multiple approaches to approximate uncertainty propagation can be found in the literature [Girard et al., 2002, Quiñonero-Candela et al., 2003].”
• Probabilistic Recurrent State-Space Models (2018, background): “Moment matching (Girard et al., 2003) Training on the full dataset is however not possible for larger datasets and provides an advantage to NIGP.”
• Dynamic systems identification with Gaussian processes (2005, methodology): “In [9], iterative multiple-step-ahead prediction is achieved by feeding back the predictive mean, as well as the predictive variance at each time-step, thus taking the uncertainty attached to each intermediate prediction into account.”