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 571580 of 641 papers

TitleStatusHype
JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms0
Joint Search of Data Augmentation Policies and Network Architectures0
Katib: A Distributed General AutoML Platform on Kubernetes0
KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI0
Large Language Model Agent for Hyper-Parameter Optimization0
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures0
Lessons learned from the AutoML challenge0
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance0
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection0
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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