Symbolic Regression

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 155 papers with code (418 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Exhaustive Symbolic Regression
  21 Nov 2022 6 repositories listed
  To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations -- made with a given basis set of operators and up to a specified maximum…
• Contemporary Symbolic Regression Methods and their Relative Performance
  29 Jul 2021 5 repositories listed Syntology ran 8 of 8 samples · 0 unverified
  We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
• Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
  2 May 2023 4 repositories listed Syntology ran 10 of 29 samples · 19 unverified
  PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep learning…
• On Neural Differential Equations
  4 Feb 2022 4 repositories listed Syntology ran 4 of 4 samples · 0 unverified
  Topics include: neural ordinary differential equations (e.
• OccamNet: A Fast Neural Model for Symbolic Regression at Scale
  16 Jul 2020 4 repositories listed
  Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of finding compact analytic expressions…
• Grammar Variational Autoencoder
  6 Mar 2017 4 repositories listed Syntology ran 4 of 19 samples · 15 unverified · 3 pointer-only (licence)
  Crucially, state-of-the-art methods often produce outputs that are not valid.
• DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces
  15 Dec 2024 3 repositories listed
  We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression.
• End-to-end symbolic regression with transformers
  22 Apr 2022 3 repositories listed Syntology ran 4 of 4 samples · 0 unverified
  Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step procedure: predicting the "skeleton" of the…
• Discovering Symbolic Models from Deep Learning with Inductive Biases
  19 Jun 2020 3 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, then we apply symbolic regression to components of the learned model to extract explicit…
• Learning a Formula of Interpretability to Learn Interpretable Formulas
  23 Apr 2020 3 repositories listed Syntology ran 3 of 4 samples · 1 unverified
  We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an…
• Improving Genetic Programming for Symbolic Regression with Equality Graphs
  29 Jan 2025 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  The equality graph is a data structure capable of compactly storing expressions and their equivalent forms allowing an efficient verification of whether an expression has been visited in any of their stored equivalent…
• EvoGP: A GPU-accelerated Framework for Tree-based Genetic Programming
  21 Jan 2025 2 repositories listed
  However, GPU acceleration of TGP faces three key challenges: inefficient tree encoding, highly heterogeneous genetic operations, and limited parallelism in fitness evaluation.
• LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
  29 Apr 2024 2 repositories listed
  Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines.
• The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
  17 Apr 2024 2 repositories listed Syntology ran 17 of 29 samples · 12 unverified
  The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a…
• Sym-Q: Adaptive Symbolic Regression via Sequential Decision-Making
  7 Feb 2024 2 repositories listed
  Our results demonstrate that Sym-Q excels not only in recovering underlying mathematical structures but also uniquely learns to efficiently refine the output expression based on reward signals, thereby discovering…
• Deep Generative Symbolic Regression
  30 Dec 2023 2 repositories listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)
  Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery.
• A Transformer Model for Symbolic Regression towards Scientific Discovery
  7 Dec 2023 2 repositories listed
  Once trained, we apply our best model to the SRSD datasets (Symbolic Regression for Scientific Discovery datasets) which yields state-of-the-art results using the normalized tree-based edit distance, at no extra…
• SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
  3 Oct 2023 2 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 1 pointer-only (licence)
  To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings.
• SymFormer: End-to-end symbolic regression using transformer-based architecture
  31 May 2022 2 repositories listed
  The task of finding formulas from a set of observed inputs and outputs is called symbolic regression.
• Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
  29 Oct 2021 2 repositories listed Syntology ran 11 of 12 samples · 1 unverified
  Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
• Chaos as an interpretable benchmark for forecasting and data-driven modelling
  11 Oct 2021 2 repositories listed
  Our dataset is annotated with known mathematical properties of each system, and we perform feature analysis to broadly categorize the diverse dynamics present across the collection.
• SymbolicGPT: A Generative Transformer Model for Symbolic Regression
  27 Jun 2021 2 repositories listed Syntology ran 9 of 9 samples · 0 unverified
  Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values.
• Neural Symbolic Regression that Scales
  11 Jun 2021 2 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs.
• AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
  18 Jun 2020 2 repositories listed Syntology ran 5 of 20 samples · 15 unverified
  We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity.
• AI Feynman: a Physics-Inspired Method for Symbolic Regression
  27 May 2019 2 repositories listed
  A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function.
• Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
  30 Jun 2025 1 repository listed
  This paper presents a search for underlying analytic structures among the fundamental parameters of the Standard Model (SM) using symbolic regression and genetic programming.
• Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
  23 Jun 2025 1 repository listed
  Once this structured latent representation is established, we exploit the symbolic regression capability of KAN to extract compact and interpretable expressions for the system's governing dynamics.
• syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
  10 Jun 2025 1 repository listed
  We seek simple symbolic parametrisations for the impact of baryonic physics on the matter power spectrum for a range of physically motivated models, as a function of wavenumber, redshift, cosmology, and parameters…
• Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression
  9 Jun 2025 1 repository listed
  Symbolic regression (SR) aims to discover closed-form mathematical expressions that accurately describe data, offering interpretability and analytical insight beyond standard black-box models.
• Improving Memory Efficiency for Training KANs via Meta Learning
  9 Jun 2025 1 repository listed Syntology ran 3 of 9 samples · 6 unverified · 9 pointer-only (licence)
  However, KANs are characterized by a substantially larger number of trainable parameters, leading to challenges in memory efficiency and higher training costs compared to MLPs.

Syntology lines on 16 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-25.