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 176–200 of 641 papers

Title	Date	Tasks	Status	Score
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study	Jul 4, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	5
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data Imbalance	Sep 22, 2024	AutoMLBenchmarking	CodeCode Available	5
Automated Imbalanced Learning	Nov 1, 2022	AutoML	CodeCode Available	5
Information Leakage Detection through Approximate Bayes-optimal Prediction	Jan 25, 2024	AutoMLLearning Theory	CodeCode Available	5
Automated Creative Optimization for E-Commerce Advertising	Feb 28, 2021	AutoMLClick-Through Rate Prediction	CodeCode Available	5
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML	Mar 2, 2021	AutoMLBIG-bench Machine Learning	CodeCode Available	5
Improving the sample-efficiency of neural architecture search with reinforcement learning	Oct 13, 2021	AutoMLDeep Learning	CodeCode Available	5
Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars	Apr 4, 2024	AutoML	CodeCode Available	5
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey	Nov 11, 2024	AutoMLFeature Engineering	CodeCode Available	5
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost	Aug 5, 2024	AutoMLDiversity	CodeCode Available	5
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models	Aug 1, 2024	AutoMLCode Generation	CodeCode Available	5
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule	Sep 16, 2023	AutoMLData Augmentation	CodeCode Available	5
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization	May 27, 2019	AutoMLimage-classification	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
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines	Feb 9, 2023	AutoMLFederated Learning	CodeCode Available	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	5
Imbalanced Regression Pipeline Recommendation	Jul 16, 2025	AutoMLMeta-Learning	CodeCode Available	5
Hyperparameter Importance Analysis for Multi-Objective AutoML	May 13, 2024	AutoMLHyperparameter Optimization	CodeCode Available	5
A knowledge-driven AutoML architecture	Nov 28, 2023	AutoML	CodeCode Available	5
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn	Jan 1, 2014	AutoMLBenchmarking	CodeCode Available	5
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment	Jan 12, 2023	AutoML	CodeCode Available	5
Benchmarking AutoML algorithms on a collection of synthetic classification problems	Dec 6, 2022	AutoMLBenchmarking	CodeCode Available	5
Auto-Keras: An Efficient Neural Architecture Search System	Jun 27, 2018	AutoMLBayesian Optimization	CodeCode Available	5
Large Language Models Synergize with Automated Machine Learning	May 6, 2024	AutoMLCode Generation	CodeCode Available	5
AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting	Dec 20, 2023	AutoMLMeta-Learning	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