Forking Uncertainties: Reliable Prediction and Model Predictive Control With Sequence Models via Conformal Risk Control-Reference-Cited by-同舟云学术

Forking Uncertainties: Reliable Prediction and Model Predictive Control With Sequence Models via Conformal Risk Control

Published:2024 Issue: Volume:5 Page:44-61
ISSN:2641-8770
Container-title:IEEE Journal on Selected Areas in Information Theory
language:
Short-container-title:IEEE J. Sel. Areas Inf. Theory

Author:

Zecchin Matteo¹^ORCID,Park Sangwoo¹^ORCID,Simeone Osvaldo¹^ORCID

Affiliation:

1. Department of Engineering, King’s Communications, Learning and Information Processing Lab, Centre for Intelligent Information Processing Systems, King’s College London, London, U.K.

Funder

European Union’s Horizon Europe Project CENTRIC

Open Fellowships of the EPSRC

U.K. Government funded Project under the Future Open Networks Research Challenge (FONRC) sponsored by the Department of Science Innovation and Technology (DSIT) through the Project REASON

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8700143/10461668/10445122.pdf?arnumber=10445122

Reference80 articles.

1. Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints

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5. Path Planning for UAV Ground Target Tracking via Deep Reinforcement Learning

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