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
Neural Architecture Search for Sentence Classification with BERTCode0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
Automated Contrastive Learning Strategy Search for Time Series—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
Automated Machine Learning for Multi-Label Classification—0
Evolving machine learning workflows through interactive AutoML—0
Principled Architecture-aware Scaling of HyperparametersCode0
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models—0
Floralens: a Deep Learning Model for the Portuguese Native Flora—0
Guided Evolution with Binary Discriminators for ML Program Search—0
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study—0
The Potential of AutoML for Recommender Systems—0
Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations—0
Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering—0
Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity—0
Large Language Model Agent for Hyper-Parameter Optimization—0
Information Leakage Detection through Approximate Bayes-optimal PredictionCode0
X Hacking: The Threat of Misguided AutoMLCode0
DREAM: Debugging and Repairing AutoML Pipelines—0
KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI—0
Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines—0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools—0
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoMLCode0
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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