SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 of 641 papers

TitleStatusHype
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
Automated Imbalanced LearningCode0
Efficient Automatic Machine Learning via Design GraphsCode0
Neural Architectural Backdoors—0
Extensible Proxy for Efficient NASCode1
Multi-Agent Automated Machine Learning—0
Sample-Then-Optimize Batch Neural Thompson SamplingCode1
AutoML for Climate Change: A Call to ActionCode1
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies—0
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI SolutionsCode1
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene—0
Efficient Non-Parametric Optimizer Search for Diverse TasksCode0
Automatic and effective discovery of quantum kernelsCode0
Industrial Data Science for Batch Manufacturing Processes—0
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat—0
Fraud Dataset Benchmark and ApplicationsCode2
A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters—0
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
Task Selection for AutoML System Evaluation—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine LearningCode0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round—0
AMLB: an AutoML BenchmarkCode2
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture—0
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All datasetsChalearn-AutoML-1Breast Cancer Coimbra Data SetOrdinalDatasetWine

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4—Unverified
2reference_mbRank (AutoML5)5.2—Unverified
3postech.mlg_exbrainRank (AutoML5)5.2—Unverified
4abhishek4Rank (AutoML5)4.6—Unverified
5referenceRank (AutoML5)4.4—Unverified
6reference_lsRank (AutoML5)4—Unverified
7djajeticRank (AutoML5)3—Unverified
8aad_freiburgRank (AutoML5)1.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02—Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33—Unverified