--- title: "Who cites Symbolic Regression is NP-hard — 7 citing papers" description: "7 papers of the Elamur Research library cite «Symbolic Regression is NP-hard», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/virgolin-2022-symbolic-regression-np-hard/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/virgolin-2022-symbolic-regression-np-hard/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Symbolic Regression is NP-hard](https://md.research.elamur.ai/free/en/papers/virgolin-2022-symbolic-regression-np-hard/) / cited by # Who cites «Symbolic Regression is NP-hard» 7 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «Symbolic Regression is NP-hard»](https://md.research.elamur.ai/free/en/papers/virgolin-2022-symbolic-regression-np-hard/) 1. [**Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl arXiv:2305.01582**Miles Cranmer (Princeton University, Flatiron Institute)*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/university-2023-interpretable-machine-learning-science/) 2. [**LLM-SR: Scientific Equation Discovery via Programming with Large Language Models arXiv:2404.18400**Parshin Shojaee, Kazem Meidani, Shashank Gupta, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/shojaee-2024-llm-sr-scientific-equation/) 3. [**LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models arXiv:2504.10415**Parshin Shojaee, Ngoc-Hieu Nguyen, Kazem Meidani, et al.*2025 · 18 citations*](https://md.research.elamur.ai/free/en/papers/shojaee-2025-llm-srbench-new-benchmark/) 4 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/virgolin-2022-symbolic-regression-np-hard/) — Symbolic Regression is NP-hard