Automated Learning of Interpretable Models with Quantified Uncertainty

Automated Learning of Interpretable Models with Quantified Uncertainty

12 April 2022

Geoffrey F. Bomarito

Patrick E. Leser

J. D. Hochhalter

Papers citing "Automated Learning of Interpretable Models with Quantified Uncertainty"

10 / 10 papers shown

Title
Contemporary Symbolic Regression Methods and their Relative Performance William La Cava Patryk Orzechowski Bogdan Burlacu Fabrício Olivetti de Francca M. Virgolin Ying Jin M. Kommenda J. Moore 167 259 0 29 Jul 2021
SymbolicGPT: A Generative Transformer Model for Symbolic Regression Mojtaba Valipour Bowen You Maysum Panju A. Ghodsi 36 93 0 27 Jun 2021
Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty Nicholas Galioto Alex Gorodetsky 53 32 0 04 Mar 2020
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients Brenden K. Petersen Mikel Landajuela T. Nathan Mundhenk Claudio Santiago Soo K. Kim Joanne T. Kim 50 314 0 10 Dec 2019
AI Feynman: a Physics-Inspired Method for Symbolic Regression S. Udrescu Max Tegmark 148 869 0 27 May 2019
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead Cynthia Rudin ELM FaML 50 219 0 26 Nov 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 77 1,090 0 31 Jul 2018
Where are we now? A large benchmark study of recent symbolic regression methods Patryk Orzechowski William La Cava J. Moore 35 160 0 25 Apr 2018
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison Randal S. Olson William La Cava Patryk Orzechowski Ryan J. Urbanowicz J. Moore 361 378 0 01 Mar 2017
Overall Objective Priors J. Berger J. Bernardo Dongchu Sun 57 118 0 10 Apr 2015