Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

Wang, H., Lenihan, P. and Wang, Z. orcid.org/0000-0001-6157-0662 (2025) Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization. In: CGO '25: Proceedings of the 23rd ACM/IEEE International Symposium on Code Generation and Optimization. CGO '25: 23rd ACM/IEEE International Symposium on Code Generation and Optimization, 01-05 Mar 2025, Las Vegas, NV, USA. ACM , New York, NY , pp. 31-46. ISBN 9798400712753

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Item Type: Proceedings Paper
Authors/Creators:
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© 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution 4.0 International License.

Keywords: Model reliability, Statistical assessment, Machine learning
Dates:
  • Published: 1 March 2025
  • Published (online): 1 March 2025
  • Accepted: 4 November 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Distributed Systems & Services
Funding Information:
Funder
Grant number
EPSRC (Engineering and Physical Sciences Research Council)
EP/X037304/1
EPSRC (Engineering and Physical Sciences Research Council)
EP/X018202/1
Depositing User: Symplectic Publications
Date Deposited: 03 Jan 2025 10:26
Last Modified: 19 Mar 2025 13:12
Published Version: https://dl.acm.org/doi/10.1145/3696443.3708959
Status: Published
Publisher: ACM
Identification Number: 10.1145/3696443.3708959
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