Learning Random Feature Dynamics for Uncertainty Quantification

Agudelo-España, Diego; Nemmour, Yassine; Schölkopf, Bernhard; Zhu, Jia-Jie

doi:10.1109/CDC51059.2022.9993152

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Learning Random Feature Dynamics for Uncertainty Quantification

MPS-Authors

Agudelo-España, Diego
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons249618

Nemmour, Yassine
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84193

Schölkopf, Bernhard
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons232844

Zhu, Jia-Jie
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://doi.org/10.1109/CDC51059.2022.9993152
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Agudelo-España, D., Nemmour, Y., Schölkopf, B., & Zhu, J.-J. (2023). Learning Random Feature Dynamics for Uncertainty Quantification. In 2022 IEEE 61st Conference on Decision and Control (CDC) (pp. 4937-4944). Piscataway, NJ: IEEE. doi:10.1109/CDC51059.2022.9993152.

Cite as: https://hdl.handle.net/21.11116/0000-000F-E552-2

Abstract

There is no abstract available