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 311320 of 641 papers

TitleStatusHype
A Survey on Multi-Objective Neural Architecture Search0
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation0
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations0
Efficient and Joint Hyperparameter and Architecture Search for Collaborative FilteringCode0
SigOpt Mulch: An Intelligent System for AutoML of Gradient Boosted Trees0
Pricing European Options with Google AutoML, TensorFlow, and XGBoostCode0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure0
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenMLCode0
AutoML in Heavily Constrained ApplicationsCode0
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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