Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
Operon c++: An efficient genetic programming framework for symbolic regression,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.
Symbolic regression provides an interpretable way to extract mathematical relationships from data for scientific discovery and surrogate modeling in the physical sciences.
citing papers explorer
-
Symbolic Classification-Enabled LHC Limits Online BSM Global Fits
Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
-
Towards symbolic regression for interpretable clinical decision scores
Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
-
Symbolic Emulators for Cosmology: Accelerating Cosmological Analyses Without Sacrificing Precision
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.
-
Introduction to Symbolic Regression in the Physical Sciences
Symbolic regression provides an interpretable way to extract mathematical relationships from data for scientific discovery and surrogate modeling in the physical sciences.