Can AutoML outperform humans? An evaluation on popular OpenML datasets
using AutoML Benchmark

v1v2 (latest)

Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark

3 September 2020

Maximilien Kintz

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Papers citing "Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark"

7 / 7 papers shown

Title
Assessing the Use of AutoML for Data-Driven Software Engineering Fabio Calefato L. Quaranta F. Lanubile Marcos Kalinowski 74 7 0 20 Jul 2023
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Nick Erickson Jonas W. Mueller Alexander Shirkov Hang Zhang Pedro Larroy Mu Li Alex Smola LMTD 218 628 0 13 Mar 2020
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools A. Truong Austin Walters Jeremy Goodsitt Keegan E. Hines Bayan Bruss R. Farivar 89 199 0 15 Aug 2019
AutoML: A Survey of the State-of-the-Art Xin He Kaiyong Zhao Xiaowen Chu 138 1,460 0 02 Aug 2019
An Open Source AutoML Benchmark Pieter Gijsbers E. LeDell Janek Thomas Sébastien Poirier B. Bischl Joaquin Vanschoren VLM 63 241 0 01 Jul 2019
Benchmark and Survey of Automated Machine Learning Frameworks Marc-André Zöller Marco F. Huber 60 86 0 26 Apr 2019
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science Randal S. Olson Nathan Bartley Ryan J. Urbanowicz J. Moore 56 530 0 20 Mar 2016