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 151–175 of 641 papers

Title	Date	Tasks	Status	Hype	Score
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data	Sep 9, 2021	AutoMLBIG-bench Machine Learning	CodeCode Available	1	5
LEMUR Neural Network Dataset: Towards Seamless AutoML	Apr 14, 2025	AutoMLBenchmarking	CodeCode Available	1	5
DC-BENCH: Dataset Condensation Benchmark	Jul 20, 2022	AutoMLData Augmentation	CodeCode Available	1	5
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning	Mar 27, 2020	AutoMLBayesian Optimization	CodeCode Available	1	5
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering	May 5, 2023	Automated Feature EngineeringAutoML	CodeCode Available	1	5
Is Mamba Capable of In-Context Learning?	Feb 5, 2024	AutoMLIn-Context Learning	CodeCode Available	1	5
DHP: Differentiable Meta Pruning via HyperNetworks	Mar 30, 2020	AutoMLDenoising	CodeCode Available	1	5
Learning meta-features for AutoML	Sep 29, 2021	AutoML	CodeCode Available	1	5
Multi-Objective Evolutionary Design of Composite Data-Driven Models	Mar 1, 2021	AutoMLBIG-bench Machine Learning	CodeCode Available	1	5
Rethinking Neural Operations for Diverse Tasks	Mar 29, 2021	AutoMLImage Classification	CodeCode Available	1	5
Direct Differentiable Augmentation Search	Apr 9, 2021	AutoMLData Augmentation	CodeCode Available	1	5
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?	Jun 12, 2020	AutoMLNeural Architecture Search	CodeCode Available	1	5
AutoML Two-Sample Test	Jun 17, 2022	AutoMLscientific discovery	CodeCode Available	1	5
AutoDC: Automated data-centric processing	Nov 23, 2021	AutoMLData Augmentation	CodeCode Available	1	5
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule	Sep 16, 2023	AutoMLData Augmentation	CodeCode Available	0	5
A Robust Experimental Evaluation of Automated Multi-Label Classification Methods	May 16, 2020	AutoMLBayesian Optimization	CodeCode Available	0	5
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization	May 27, 2019	AutoMLimage-classification	CodeCode Available	0	5
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML	Dec 11, 2023	AutoMLfeature selection	CodeCode Available	0	5
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment	Jan 12, 2023	AutoML	CodeCode Available	0	5
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	5
A-DARTS: Stable Model Selection for Data Repair in Time Series	May 1, 2025	AutoMLImputation	CodeCode Available	0	5
Imbalanced Regression Pipeline Recommendation	Jul 16, 2025	AutoMLMeta-Learning	CodeCode Available	0	5
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines	Feb 9, 2023	AutoMLFederated Learning	CodeCode Available	0	5
AMDet: A Tool for Mitotic Cell Detection in Histopathology Slides	Aug 8, 2021	AutoMLCell Detection	CodeCode Available	0	5
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	5

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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