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http://hdl.handle.net/1942/38912
Title: | Tree-structured Parzen estimators with uncertainty for hyperparameter optimization of machine learning algorithms | Authors: | MORALES HERNANDEZ, Alejandro VAN NIEUWENHUYSE, Inneke ROJAS GONZALEZ, Sebastian |
Issue Date: | 2022 | Source: | Proceedings of the 2022 Winter Simulation Conference, | Series/Report: | Network modeling analysis in health informatics and bioinformatics | Series/Report no.: | online | Status: | Early view | Abstract: | Hyperparameter optimization (HPO) is one of the first tasks to be performed during the application of Machine Learning (ML) algorithms to real problems. Tree-structured Parzen estimators (TPE) have demonstrated their ability to find hyperparameter configurations in high dimensions with efficient evaluation budgets. However, as is common in HPO procedures, TPE ignores the fact that the expected performance of the algorithm, for any given HPO configuration, is affected by uncertainty. Building on the TPE algorithm proposed by Bergstra et al. (2011), we propose a strategy to account for this uncertainty and show that its management leads to better algorithm performance. | Document URI: | http://hdl.handle.net/1942/38912 | Category: | C2 | Type: | Proceedings Paper |
Appears in Collections: | Research publications |
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TPE_with_uncertainty__WSC2022_ Updated.pdf Restricted Access | Early view | 95.07 kB | Adobe PDF | View/Open Request a copy |
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