| | 1 | = Symbolic Regression Workshop (!SymReg) = |
| | 2 | |
| | 3 | Part of GECCO 2022] \\ |
| | 4 | July 09th - 13th, 2022 \\ |
| | 5 | Boston, USA (hybrid event) |
| | 6 | |
| | 7 | Submission Deadline: April 11, 2022 |
| | 8 | |
| | 9 | |
| | 10 | https://gecco-2022.sigevo.org \\ |
| | 11 | https://gecco-2022.sigevo.org/Workshops#SymReg |
| | 12 | [[BR]][[BR]] |
| | 13 | |
| | 14 | == Scope == |
| | 15 | 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. However, 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. |
| | 16 | [[BR]] |
| | 17 | |
| | 18 | 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 [https://gecco-2022.sigevo.org/Competitions#id_Interpretable%20Symbolic%20Regression%20for%20Data%20Science symbolic regression competition] to present their algorithms and results in detail at this workshop. |
| | 19 | [[BR]] |
| | 20 | |
| | 21 | Particular topics of interest include, but are not limited to: |
| | 22 | * Evolutionary and non-evolutionary algorithms for symbolic regression |
| | 23 | * Improving stability of symbolic regression algorithms |
| | 24 | * Integration of side-information (physical laws, constraints, ...) |
| | 25 | * Benchmarking symbolic regression algorithms |
| | 26 | * Symbolic regression for scientific machine learning |
| | 27 | * Innovative symbolic regression applications |
| | 28 | |
| | 29 | == Important Dates == |
| | 30 | |
| | 31 | || Submission opening: || '''February, 11, 2002''' || |
| | 32 | || Submission deadline: || '''April 11th, 2022 (strict!)''' || |
| | 33 | || Notification of acceptance: || '''April 25th, 2022''' || |
| | 34 | || Camera-ready submission: || '''May 2nd, 2022''' || |
| | 35 | || Workshop at GECCO 2022: || '''July 9th or 10th, 2022''' || |
| | 36 | |
| | 37 | |
| | 38 | == Additional Details == |
| | 39 | Submitted papers must not exceed 8 pages and must adhere to GECCO's [https://gecco-2022.sigevo.org/Paper-Submission-Instructions paper submission instructions]. All submissions should be anonymized and be made via [https://ssl.linklings.net/conferences/gecco/ linklings]. Accepted papers must be presented at the workshop and will be published as part of a companion volume to the conference proceedings in the ACM Digital library. |
| | 40 | [[BR]] |
| | 41 | |
| | 42 | Due to the current worldwide crisis regarding COVID-19, GECCO 2022 will be held as a hybrid conference. All participants can attend either onsite or online and all onsite sessions are also streamed online. |
| | 43 | |
| | 44 | == Organizing Commitee == |
| | 45 | Michael Kommenda - University of Applied Sciences Upper Austria \\ |
| | 46 | William La Cava - Boston Children’s Hospital and Harvard Medical School \\ |
| | 47 | Gabriel Kronberger - University of Applied Sciences Upper Austria \\ |
| | 48 | Steven Gustafson - Noonum, Inc \\ |
