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Authors
Mehta, Nikhil https://orcid.org/0000-0002-7277-8466
Lorraine, Jonathan https://orcid.org/0000-0002-1255-6554
Masson, Steve https://orcid.org/0009-0003-3803-9760
Arunachalam, Ramanathan https://orcid.org/0000-0001-8934-0981
Bhat, Zaid Pervaiz https://orcid.org/0009-0009-3950-5456
Lucas, James https://orcid.org/0009-0005-4580-7937
Zachariah, Arun George https://orcid.org/0000-0002-5608-9089
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Chapter History
First Online: 12 May 2025
Ethics Statement
: The approach presented in this paper facilitates machine learning research and applications by making it easier to find performant hyperparameters. Designing more efficient HPO can help lower the cost of model training (e.g., time, compute, and environmental impact), making machine learning experiments easier for those in other disciplines. Overall, the benefits and risks are likely similar to those of other automated machine learning (AutoML) research.
: NVIDIA funded this work. Jonathan Lorraine received funding from student scholarships at the University of Toronto and the Vector Institute, which do not directly support this work.
Conference Information
Conference Acronym: ECCV
Conference Name: European Conference on Computer Vision
Conference City: Milan
Conference Country: Italy
Conference Year: 2024
Conference Start Date: 29 September 2024
Conference End Date: 4 October 2024
Conference Number: 18
Conference ID: eccv2024
Conference URL: https://eccv2024.ecva.net/
