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
Neural Architecture Search for Sentence Classification with BERT	Mar 27, 2024	AutoMLClassification	CodeCode Available
Auto-nnU-Net: Towards Automated Medical Image Segmentation	May 22, 2025	AutoMLComputational Efficiency	CodeCode Available
Neuraxle - A Python Framework for Neat Machine Learning Pipelines	Sep 1, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch	Mar 6, 2020	AutoMLBIG-bench Machine Learning	CodeCode Available
AutoML using Metadata Language Embeddings	Oct 8, 2019	AutoML	CodeCode Available
Two-stage Optimization for Machine Learning Workflow	Jul 1, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge	Jul 28, 2021	AutoMLFeature Engineering	CodeCode Available
AutoML in Heavily Constrained Applications	Jun 29, 2023	AutoMLMeta-Learning	CodeCode Available
AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification	Aug 19, 2024	AutoMLClassification	CodeCode Available
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures	Feb 26, 2023	AutoML	CodeCode Available
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning	Feb 13, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models	Oct 9, 2024	AutoMLModel Selection	CodeCode Available
A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools	Jan 14, 2021	AutoMLBIG-bench Machine Learning	CodeCode Available
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning	Aug 8, 2022	AutoMLMeta-Learning	CodeCode Available
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization	Jul 1, 2019	AutoMLGPU	CodeCode Available
Classification of integers based on residue classes via modern deep learning algorithms	Apr 3, 2023	AutoMLFeature Engineering	CodeCode Available

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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