GECCO 2023 Symbolic Regression Workshop (SymReg)
Part of GECCO 2023
July 15th - 19th, 2023
Lisbon, Portugal (hybrid event)
Submission Deadline: April 14th, 2023
Additional Information on GECCO:
https://gecco-2023.sigevo.org
https://gecco-2023.sigevo.org/Workshops#SymReg
Scope
Symbolic regression designates the search for symbolic models that describe a relationship in provided data. Symbolic regression has been one of the first applications of genetic programming and as such is tightly connected to evolutionary algorithms. In recent years several non-evolutionary techniques for solving symbolic regression have emerged. Especially with the focus on interpretability and explainability in AI research, symbolic regression takes a leading role among machine learning methods, whenever model inspection and understanding by a domain expert is desired. Examples where symbolic regression already produces outstanding results include modeling where interpretability is desired, modeling of non-linear dependencies, modeling with small data sets or noisy data, modeling with additional constraints, or modeling of differential equation systems.
The focus of this workshop is to further advance the state-of-the-art in symbolic regression by gathering experts in the field of symbolic regression and facilitating an exchange of novel research ideas. Therefore, we encourage submissions presenting novel techniques or applications of symbolic regression, theoretical work on issues of generalization, size and interpretability of the models produced, or algorithmic improvements to make the techniques more efficient, more reliable and generally better controlled. Furthermore, we invite participants of the symbolic regression competition to present their algorithms and results in detail at this workshop.
Particular topics of interest include, but are not limited to:- Evolutionary and non-evolutionary algorithms for symbolic regression
- Improving stability of symbolic regression algorithms
- Model selection
- Uncertainty quantification
- Integration of side-information (physical laws, constraints, ...)
- Benchmarking symbolic regression algorithms
- Symbolic regression for scientific machine learning
- Innovative symbolic regression applications
Important Dates
- Submission opening: February 13th, 2023
- Submission deadline: April 14th, 2023
- Notification of acceptance: May 3rd, 2023
- Camera-ready submission: May 10th, 2023
- Workshop at GECCO 2023: July 15th or 16th, 2023
Previous Workshops
2022 in Boston
- Uncertainty in Equation Learning, Matthias Werner, Andrej Junginger, Philipp Henning, Georg Martius
- Bingo: A Customizable Framework for Symbolic Regression with Genetic Programming, David Randall, Tyler Townsend, Jacob Hochhalter, Geoffrey Bomarito
- Interaction-Transformation Evolutionary Algorithm with Coefficients Optimization, Guilherme Imai Aldeia, Fabricio de Franca
- Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression, Marco Virgolin, Peter Bosman
- Genetic Programming with Stochastic Gradient Descent Revisited: Initial Findings on SRBench, Grant Dick
- Invited Talk From the Winner of the Symbolic Regression Competition
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Organizing Commitee
Michael Kommenda - University of Applied Sciences Upper Austria
michael <dot> kommenda <at> fh-ooe <dot> at
William La Cava - Boston Children’s Hospital and Harvard Medical School
Gabriel Kronberger - University of Applied Sciences Upper Austria
Steven Gustafson - Noonum, Inc
