Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression?

Author:

Harrison Joe12ORCID,Virgolin Marco1ORCID,Alderliesten Tanja3ORCID,Bosman Peter12ORCID

Affiliation:

1. Centrum Wiskunde & Informatica (CWI), Amsterdam, Netherlands

2. Delft University of Technology, Amsterdam, Netherlands

3. Leiden University, Leiden, Netherlands

Funder

NWO

Publisher

ACM

Reference56 articles.

1. 117th US Congress. 2022 . Algorithmic accountability act. https://www.congress.gov/bill/117th-congress/house-bill/6580/ 117th US Congress. 2022. Algorithmic accountability act. https://www.congress.gov/bill/117th-congress/house-bill/6580/

2. Arthur Asuncion and David Newman. 2007. UCI machine learning repository. Arthur Asuncion and David Newman. 2007. UCI machine learning repository.

3. Genetic programming as a model induction engine

4. Deaglan J Bartlett , Harry Desmond , and Pedro G Ferreira . 2022. Exhaustive Symbolic Regression. arXiv preprint arXiv:2211.11461 ( 2022 ). Deaglan J Bartlett, Harry Desmond, and Pedro G Ferreira. 2022. Exhaustive Symbolic Regression. arXiv preprint arXiv:2211.11461 (2022).

5. Luca Biggio , Tommaso Bendinelli , Alexander Neitz , Aurelien Lucchi , and Giambattista Parascandolo . 2021 . Neural symbolic regression that scales . In International Conference on Machine Learning. PMLR, 936--945 . Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, and Giambattista Parascandolo. 2021. Neural symbolic regression that scales. In International Conference on Machine Learning. PMLR, 936--945.

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