58 citations · 112 across the 17 of their papers we have counts for
7 papers · 1 filter
Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression
Julia Reuter, Fabricio Olivetti de Franca
Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationshi…
rEGGression: an Interactive and Agnostic Tool for the Exploration of Symbolic Regression Models
Fabricio Olivetti de Franca, Gabriel Kronberger
Regression analysis is used for prediction and to understand the effect of independent variables on dependent variables. Symbolic regression (SR) automates the search for non-linea…
Improving Genetic Programming for Symbolic Regression with Equality Graphs
Fabricio Olivetti de Franca, Gabriel Kronberger
The search for symbolic regression models with genetic programming (GP) has a tendency of revisiting expressions in their original or equivalent forms. Repeatedly evaluating equiva…
Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set
Guilherme Seidyo Imai Aldeia, Fabricio Olivetti de Franca
In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the predictio…
Multi-View Symbolic Regression
Etienne Russeil, Fabrício Olivetti de França, Konstantin Malanchev +6
Symbolic regression (SR) searches for analytical expressions representing the relationship between a set of explanatory and response variables. Current SR methods assume a single d…
A Hash-based Co-Clustering Algorithm for Categorical Data
Fabricio Olivetti de França
Many real-life data are described by categorical attributes without a pre-classification. A common data mining method used to extract information from this type of data is clusteri…
Iterative Universal Hash Function Generator for Minhashing
Fabricio Olivetti de Franca
Minhashing is a technique used to estimate the Jaccard Index between two sets by exploiting the probability of collision in a random permutation. In order to speed up the computati…