mlsanityData to 7 Oct 2026

Symbolic-Regression Boosting

arXiv 2206.12082 ↗First posted 24 Jun 2022cs.NE cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 2 of its 24 references that are in the map. First posted on arXiv 24 Jun 2022.

Builds on

2 of 24 references.

References · 2

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2018Where are we now? A large benchmark study of recent symbolic regression methodscited by 44

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.