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 51–75 of 641 papers

TitleStatusHype
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree SearchCode1
Towards Automated Model Design on Recommender SystemsCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space ModelsCode1
CliMB: An AI-enabled Partner for Clinical Predictive ModelingCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce ApplicationsCode1
Automated Machine Learning in InsuranceCode1
Fast Optimizer BenchmarkCode1
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation FrameworkCode1
LLM Guided Evolution - The Automation of Models Advancing ModelsCode1
Robustifying and Boosting Training-Free Neural Architecture SearchCode1
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision TasksCode1
Retrieve, Merge, Predict: Augmenting Tables with Data LakesCode1
Is Mamba Capable of In-Context Learning?Code1
auto-sktime: Automated Time Series ForecastingCode1
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep CentersCode1
Embedding in Recommender Systems: A SurveyCode1
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature RankingCode1
PFNs4BO: In-Context Learning for Bayesian OptimizationCode1
Deep Pipeline Embeddings for AutoMLCode1
XTab: Cross-table Pretraining for Tabular TransformersCode1
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
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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