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 251–275 of 641 papers

Title	Date	Tasks	Status	Score
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis	May 11, 2022	AutoMLNeural Architecture Search	CodeCode Available	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	5
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options	Feb 18, 2025	AutoMLDiversity	CodeCode Available	5
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures	Feb 26, 2023	AutoML	CodeCode Available	5
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML	Jan 28, 2020	AutoML	CodeCode Available	5
AutoML for Multi-Class Anomaly Compensation of Sensor Drift	Feb 26, 2025	AutoMLEnsemble Learning	CodeCode Available	5
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning	Feb 13, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	5
Fast and Informative Model Selection using Learning Curve Cross-Validation	Nov 27, 2021	AutoMLModel Selection	CodeCode Available	5
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML	Jun 15, 2023	AutoMLDecision Making	CodeCode Available	5
Imbalanced Regression Pipeline Recommendation	Jul 16, 2025	AutoMLMeta-Learning	CodeCode Available	5
AutoML: Exploration v.s. Exploitation	Dec 23, 2019	AutoMLMeta-Learning	CodeCode Available	5
ATM: A distributed, collaborative, scalable system for automated machine learning	Dec 11, 2017	AutoMLBIG-bench Machine Learning	CodeCode Available	5
Exploring Robust Architectures for Deep Artificial Neural Networks	Jun 30, 2021	AutoMLimage-classification	CodeCode Available	5
AM-LFS: AutoML for Loss Function Search	May 17, 2019	AutoML	CodeCode Available	5
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution	Apr 1, 2020	AutoMLBIG-bench Machine Learning	CodeCode Available	5
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach	Jun 7, 2024	AutoML	CodeCode Available	5
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechia	Nov 13, 2023	AutoMLTime Series	CodeCode Available	5
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML	Jul 1, 2023	AutoMLPrediction	CodeCode Available	5
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science	Mar 20, 2016	Automated Feature EngineeringAutoML	CodeCode Available	5
Encoding high-cardinality string categorical variables	Jul 3, 2019	AutoMLFeature Engineering	CodeCode Available	5
Evolutionary Architecture Search for Graph Neural Networks	Sep 21, 2020	AutoMLNeural Architecture Search	CodeCode Available	5
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data	Aug 20, 2023	AutoML	CodeCode Available	5
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation	Nov 22, 2023	AutoMLfeature selection	CodeCode Available	5
Efficient Automatic Machine Learning via Design Graphs	Oct 21, 2022	AutoMLGraph Classification	CodeCode Available	5
Efficient Deep Learning Board: Training Feedback Is Not All You Need	Oct 17, 2024	AllAutoML	CodeCode Available	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