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 276–300 of 641 papers

Title	Date	Tasks	Status	Score
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution	Apr 1, 2020	AutoMLBIG-bench Machine Learning	CodeCode Available	5
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation	Nov 22, 2023	AutoMLfeature selection	CodeCode Available	5
Exploring Robust Architectures for Deep Artificial Neural Networks	Jun 30, 2021	AutoMLimage-classification	CodeCode Available	5
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML	Jun 15, 2023	AutoMLDecision Making	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
Automatic selection of clustering algorithms using supervised graph embedding	Nov 16, 2020	AutoMLClustering	CodeCode Available	5
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering	Jul 12, 2023	AutoMLCollaborative Filtering	CodeCode Available	5
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science	Mar 20, 2016	Automated Feature EngineeringAutoML	CodeCode Available	5
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning	May 28, 2019	AutoMLGPU	CodeCode Available	5
Evolutionary Architecture Search for Graph Neural Networks	Sep 21, 2020	AutoMLNeural Architecture Search	CodeCode Available	5
AutoML: Exploration v.s. Exploitation	Dec 23, 2019	AutoMLMeta-Learning	CodeCode Available	5
Automatic Gradient Boosting	Jul 10, 2018	AutoMLBIG-bench Machine Learning	CodeCode Available	5
A Scalable AutoML Approach Based on Graph Neural Networks	Oct 29, 2021	AutoMLGraph Generation	CodeCode Available	5
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning	Apr 26, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	5
Encoding high-cardinality string categorical variables	Jul 3, 2019	AutoMLFeature Engineering	CodeCode Available	5
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing	Dec 1, 2020	AutoMLBIG-bench Machine Learning	—Unverified	0
Automatic deep learning for trend prediction in time series data	Sep 17, 2020	AutoMLDeep Learning	—Unverified	0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making	Mar 19, 2024	AutoMLBinary Classification	—Unverified	0
Data Readiness Report	Oct 14, 2020	AutoMLManagement	—Unverified	0
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models	Feb 20, 2024	AutoML	—Unverified	0
Automatic Componentwise Boosting: An Interpretable AutoML System	Sep 12, 2021	AutoMLFeature Importance	—Unverified	0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods	Mar 13, 2024	AutoMLData Augmentation	—Unverified	0
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics	Oct 21, 2024	AutoMLData Augmentation	—Unverified	0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems	Jan 11, 2022	AutoMLMeta-Learning	—Unverified	0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation	Jul 14, 2023	AutoMLModel Selection	—Unverified	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