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 226–250 of 641 papers

Title	Date	Tasks	Status	Score
Auto-nnU-Net: Towards Automated Medical Image Segmentation	May 22, 2025	AutoMLComputational Efficiency	CodeCode Available	5
AutoDispNet: Improving Disparity Estimation With AutoML	May 17, 2019	AutoMLBayesian Optimization	CodeCode Available	5
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch	Mar 6, 2020	AutoMLBIG-bench Machine Learning	CodeCode Available	5
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI Researchers	Oct 22, 2024	Automated Feature EngineeringAutoML	CodeCode Available	5
Confidence Interval Estimation of Predictive Performance in the Context of AutoML	Jun 12, 2024	AutoML	CodeCode Available	5
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML	Jun 15, 2023	AutoMLDecision Making	CodeCode Available	5
AutoML using Metadata Language Embeddings	Oct 8, 2019	AutoML	CodeCode Available	5
Fast and Informative Model Selection using Learning Curve Cross-Validation	Nov 27, 2021	AutoMLModel Selection	CodeCode Available	5
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search	Nov 27, 2019	AutoMLNeural Architecture Search	CodeCode Available	5
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options	Feb 18, 2025	AutoMLDiversity	CodeCode Available	5
Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars	Apr 4, 2024	AutoML	CodeCode Available	5
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science	Mar 20, 2016	Automated Feature EngineeringAutoML	CodeCode Available	5
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge	Jul 28, 2021	AutoMLFeature Engineering	CodeCode Available	5
Evolutionary Architecture Search for Graph Neural Networks	Sep 21, 2020	AutoMLNeural Architecture Search	CodeCode Available	5
Encoding high-cardinality string categorical variables	Jul 3, 2019	AutoMLFeature Engineering	CodeCode Available	5
Auto-Classifier: A Robust Defect Detector Based on an AutoML Head	Sep 3, 2020	AllAutoML	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
AutoML in Heavily Constrained Applications	Jun 29, 2023	AutoMLMeta-Learning	CodeCode Available	5
AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification	Aug 19, 2024	AutoMLClassification	CodeCode Available	5
Pricing European Options with Google AutoML, TensorFlow, and XGBoost	Jul 2, 2023	AutoMLCloud Computing	CodeCode Available	5
Principled Architecture-aware Scaling of Hyperparameters	Feb 27, 2024	AutoML	CodeCode Available	5
Efficient Non-Parametric Optimizer Search for Diverse Tasks	Sep 27, 2022	AutoMLMath	CodeCode Available	5
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution	Apr 1, 2020	AutoMLBIG-bench Machine Learning	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

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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