ArticleAdvances in neural information processing systems2021
Contemporary Symbolic Regression Methods and their Relative Performance.
Article in Advances in neural information processing systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Introduction to using symbolic regression for interpretable machine learning in healthcare.Communications medicine · 2026Review
- Article
- Moving from table to graph in physics-informed spatio-temporal symbolic regression.Scientific reports · 2026Article
- Intuitive Risk Equation for Post-Transplant Bloodstream Infection Prediction: A Symbolic Regression Approach.Biomedicines · 2026Article
- Interpretable machine learning for shoreline forecasting.Scientific reports · 2026Article
- Knowledge integration for physics-informed symbolic regression using pre-trained large language models.Scientific reports · 2026Article
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- Discovering physical laws with parallel symbolic enumeration.Nature computational science · 2026Article
- SRBench++ : principled benchmarking of symbolic regression with domain-expert interpretation.IEEE transactions on evolutionary computation : a publication of the IEEE Neural Networks Council · 2025Article
- Learning interpretable network dynamics via universal neural symbolic regression.Nature communications · 2025Article
- Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship.PLOS digital health · 2024Article
- Achieving Occam's razor: Deep learning for optimal model reduction.PLoS computational biology · 2024Article
- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
- A flexible symbolic regression method for constructing interpretable clinical prediction models.NPJ digital medicine · 2023Article
- Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Identifying regulation with adversarial surrogates.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science.Computational toxicology (Amsterdam, Netherlands) · 2023Article
- A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge.Scientific reports · 2023Article
- Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers.Science advances · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. We address this shortcoming by introducing an open-source, reproducible benchmarking platform for symbolic regression. We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems. Our assessment includes both real-world datasets with no known model form as well as ground-truth benchmark problems. For the real-world datasets, we benchmark the ability of each method to learn models with low error and low complexity relative to state-of-the-art machine learning methods. For the synthetic problems, we assess each method's ability to find exact solutions in the presence of varying levels of noise. Under these controlled experiments, we conclude that the best performing methods for real-world regression combine genetic algorithms with parameter estimation and/or semantic search drivers. When tasked with recovering exact equations in the presence of noise, we find that several approaches perform similarly. We provide a detailed guide to reproducing this experiment and contributing new methods, and encourage other researchers to collaborate with us on a common and living symbolic regression benchmark.
Identifiers
38715933PMC11074949What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.