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 76–100 of 641 papers

TitleStatusHype
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
Variation in prediction accuracy due to randomness in data division and fair evaluation using interval estimation—0
Automated Machine Learning in InsuranceCode1
AutoML-guided Fusion of Entity and LLM-based Representations for Document ClassificationCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction—0
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
10 Years of Fair Representations: Challenges and Opportunities—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
Fast Optimizer BenchmarkCode1
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations—0
GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data AnalysisCode2
Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML—0
Confidence Interval Estimation of Predictive Performance in the Context of AutoMLCode0
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML ApproachCode0
Are Large Language Models the New Interface for Data Pipelines?—0
Position: A Call to Action for a Human-Centered AutoML Paradigm—0
AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML—0
DeepMol: An Automated Machine and Deep Learning Framework for Computational ChemistrCode2
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
Using Combinatorial Optimization to Design a High quality LLM Solution—0
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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