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

Showing 201210 of 641 papers

TitleStatusHype
Google Vizier: A Service for Black-Box OptimizationCode0
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-LearnCode0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular DataCode0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
AutoRL Hyperparameter LandscapesCode0
AutoRec: An Automated Recommender SystemCode0
AutoQML: A Framework for Automated Quantum Machine LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified