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 231240 of 641 papers

TitleStatusHype
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration0
Constructing a meta-learner for unsupervised anomaly detection0
AutoHAS: Efficient Hyperparameter and Architecture Search0
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data0
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization0
DivBO: Diversity-aware CASH for Ensemble Learning0
DREAM: Debugging and Repairing AutoML Pipelines0
Efficient Multi-stage Inference on Tabular Data0
A Very Brief and Critical Discussion on AutoML0
10 Years of Fair Representations: Challenges and Opportunities0
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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