AutoML

Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)

Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 626–641 of 641 papers

Title	Date	Tasks	Status	Hype
Autostacker: A Compositional Evolutionary Learning System	Mar 2, 2018	AutoMLBIG-bench Machine Learning	—Unverified	0
AMC: AutoML for Model Compression and Acceleration on Mobile Devices	Feb 10, 2018	AutoMLGPU	CodeCode Available	2
Layered TPOT: Speeding up Tree-based Pipeline Optimization	Jan 18, 2018	Automated Feature EngineeringAutoML	CodeCode Available	3
AMLA: an AutoML frAmework for Neural Network Design	Jan 1, 2018	AutoMLHyperparameter Optimization	—Unverified	0
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System	Jan 1, 2018	AutoMLBIG-bench Machine Learning	—Unverified	0
ATM: A distributed, collaborative, scalable system for automated machine learning	Dec 11, 2017	AutoMLBIG-bench Machine Learning	CodeCode Available	0
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure	Oct 9, 2017	AutoMLBIG-bench Machine Learning	—Unverified	0
autoBagging: Learning to Rank Bagging Workflows with Metalearning	Jun 28, 2017	AutoMLBayesian Optimization	—Unverified	0
Google Vizier: A Service for Black-Box Optimization	Jan 1, 2017	AutoMLBIG-bench Machine Learning	CodeCode Available	0
Automatic Frankensteining: Creating Complex Ensembles Autonomously	Jan 1, 2017	AutoMLBIG-bench Machine Learning	—Unverified	0
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning	Sep 21, 2016	AutoMLBIG-bench Machine Learning	CodeCode Available	0
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool	Jul 29, 2016	AutoMLBIG-bench Machine Learning	CodeCode Available	0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science	Mar 20, 2016	Automated Feature EngineeringAutoML	CodeCode Available	0
Efficient and Robust Automated Machine Learning	Dec 1, 2015	AutoMLBayesian Optimization	CodeCode Available	3
AutoCompete: A Framework for Machine Learning Competitions	Jul 8, 2015	AutoMLBIG-bench Machine Learning	—Unverified	0
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn	Jan 1, 2014	AutoMLBenchmarking	CodeCode Available	0

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All datasets Chalearn-AutoML-1 Breast Cancer Coimbra Data Set OrdinalDataset Wine

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	marc.boulle	Rank (AutoML5)	6.4	—	Unverified
2	reference_mb	Rank (AutoML5)	5.2	—	Unverified
3	postech.mlg_exbrain	Rank (AutoML5)	5.2	—	Unverified
4	abhishek4	Rank (AutoML5)	4.6	—	Unverified
5	reference	Rank (AutoML5)	4.4	—	Unverified
6	reference_ls	Rank (AutoML5)	4	—	Unverified
7	djajetic	Rank (AutoML5)	3	—	Unverified
8	aad_freiburg	Rank (AutoML5)	1.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Logistic Regression	Accuracy	97.02	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Zero-shot-BERT-SORT	1:1 Accuracy	55	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Logistic Regression	accuracy	98.33	—	Unverified