Conformal Prediction for STL Runtime Verification

Author:

Lindemann Lars1ORCID,Qin Xin2ORCID,Deshmukh Jyotirmoy V.2ORCID,Pappas George J.1ORCID

Affiliation:

1. University of Pennsylvania, Philadelphia, United States of America

2. University of Southern California, Los Angeles, United States of America

Funder

NSF award

National Science Foundation

Toyota R&D

Airbus Institute for Engineering Research

Northrop Grumman Aerospace Systems

Publisher

ACM

Reference87 articles.

1. Prithvi Akella , Mohamadreza Ahmadi , and Aaron D Ames . 2022. A scenario approach to risk-aware safety-critical system verification. arXiv preprint arXiv:2203.02595 ( 2022 ). Prithvi Akella, Mohamadreza Ahmadi, and Aaron D Ames. 2022. A scenario approach to risk-aware safety-critical system verification. arXiv preprint arXiv:2203.02595 (2022).

2. Prithvi Akella , Anushri Dixit , Mohamadreza Ahmadi , Joel W Burdick , and Aaron D Ames . 2022. Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification. arXiv preprint arXiv:2204.09833 ( 2022 ). Prithvi Akella, Anushri Dixit, Mohamadreza Ahmadi, Joel W Burdick, and Aaron D Ames. 2022. Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification. arXiv preprint arXiv:2204.09833 (2022).

3. Online Verification of Automated Road Vehicles Using Reachability Analysis

4. Anastasios N Angelopoulos and Stephen Bates . 2021. A gentle introduction to conformal prediction and distribution-free uncertainty quantification. arXiv preprint arXiv:2107.07511 ( 2021 ). Anastasios N Angelopoulos and Stephen Bates. 2021. A gentle introduction to conformal prediction and distribution-free uncertainty quantification. arXiv preprint arXiv:2107.07511 (2021).

5. Accelerated Learning of Predictive Runtime Monitors for Rare Failure

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