Symbolic regression
PulseAugur coverage of Symbolic regression — every cluster mentioning Symbolic regression across labs, papers, and developer communities, ranked by signal.
6 day(s) with sentiment data
-
New benchmark ERBench evaluates equation discovery algorithms
Researchers have introduced ERBench, a new benchmark and test suite specifically designed to evaluate algorithms for equation discovery. This framework focuses on assessing how well these algorithms can recover known gr…
-
Survey paper highlights need for uncertainty quantification in symbolic regression
A new survey paper addresses the critical gap in uncertainty quantification (UQ) for symbolic regression (SR) methods. The paper aims to introduce UQ concepts and review existing literature, categorizing current researc…
-
Symbolic regression method introduces partial parameter sharing
Researchers have developed a new method for symbolic regression that allows for partial parameter sharing across multiple categorical variables. This approach enables the discovery of single expressions that can describ…
-
New GP-GOMEA method optimizes expression structure and constants
Researchers have developed a new approach to symbolic regression using genetic programming, a method for constructing symbolic expressions that fit data. Their novel technique simultaneously optimizes both the structure…
-
New SAGE-Fit framework enhances symbolic regression accuracy
Researchers have developed SAGE-Fit, a new framework designed to improve symbolic regression (SR) by addressing the issue of poor parameter optimization. Existing SR methods often struggle with non-convex inner loops, l…
-
New method tackles outliers in symbolic regression
Researchers have developed a new method called Diversified Residual Symbolic Regression (DRSR) to address the challenge of outliers in symbolic regression tasks. Traditional methods struggle to identify underlying patte…