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 501–525 of 641 papers

TitleStatusHype
Improving generalisation of AutoML systems with dynamic fitness evaluations—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Incorporating domain knowledge into neural-guided search—0
Incorporating domain knowledge into neural-guided search via in situ priors and constraints—0
Incremental Search Space Construction for Machine Learning Pipeline Synthesis—0
Industrial Data Science for Batch Manufacturing Processes—0
Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines—0
Interpret-able feedback for AutoML systems—0
Iterative Compression of End-to-End ASR Model using AutoML—0
JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization—0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms—0
Joint Search of Data Augmentation Policies and Network Architectures—0
Katib: A Distributed General AutoML Platform on Kubernetes—0
KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI—0
Large Language Model Agent for Hyper-Parameter Optimization—0
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation ToolCode0
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine LearningCode0
Imbalanced Regression Pipeline RecommendationCode0
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning PipelinesCode0
On the Security Risks of AutoMLCode0
Improve Deep Forest with Learnable Layerwise Augmentation Policy ScheduleCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
AutoML for Multi-Class Anomaly Compensation of Sensor DriftCode0
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous EnvironmentCode0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
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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