# Symbolic Regression

109 papers with code • 0 benchmarks • 3 datasets

producing a mathematical expression (symbolic expression) that fits a given tabular data.

## Benchmarks

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## Libraries

Use these libraries to find Symbolic Regression models and implementations## Most implemented papers

# Exhaustive Symbolic Regression

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 complexity -- and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints.

# OccamNet: A Fast Neural Model for Symbolic Regression at Scale

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 to describe scientific data.

# Contemporary Symbolic Regression Methods and their Relative Performance

We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.

# On Neural Differential Equations

Topics include: neural ordinary differential equations (e. g. for hybrid neural/mechanistic modelling of physical systems); neural controlled differential equations (e. g. for learning functions of irregular time series); and neural stochastic differential equations (e. g. to produce generative models capable of representing complex stochastic dynamics, or sampling from complex high-dimensional distributions).

# Grammar Variational Autoencoder

Crucially, state-of-the-art methods often produce outputs that are not valid.

# Learning a Formula of Interpretability to Learn Interpretable Formulas

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 ML training process to directly optimize for interpretability.

# Discovering Symbolic Models from Deep Learning with Inductive Biases

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 physical relations.

# End-to-end symbolic regression with transformers

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 expression up to the choice of numerical constants, then fitting the constants by optimizing a non-convex loss function.

# Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

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 packages.

# AI Feynman: a Physics-Inspired Method for Symbolic Regression

A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function.