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 151–200 of 641 papers

TitleStatusHype
MetaPoison: Practical General-purpose Clean-label Data PoisoningCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Architecture Disentanglement for Deep Neural NetworksCode1
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep LearningCode1
MixPath: A Unified Approach for One-shot Neural Architecture SearchCode1
Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept DriftCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
Once-for-All: Train One Network and Specialize it for Efficient DeploymentCode1
AutoML: A Survey of the State-of-the-ArtCode1
MixConv: Mixed Depthwise Convolutional KernelsCode1
AutoSF: Searching Scoring Functions for Knowledge Graph EmbeddingCode1
MetaPruning: Meta Learning for Automatic Neural Network Channel PruningCode1
Evolutionary Neural AutoML for Deep LearningCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
Imbalanced Regression Pipeline RecommendationCode0
Optimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage—0
Multimodal Representation Learning and Fusion—0
Overtuning in Hyperparameter OptimizationCode0
CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction—0
Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation—0
VirnyFlow: A Design Space for Responsible Model DevelopmentCode0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
ZeroML: A Next Generation AutoML Language—0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
SEAL: Searching Expandable Architectures for Incremental Learning—0
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning—0
When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI TriggerCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
United States Road Accident Prediction using Random Forest Predictor—0
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures—0
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization—0
AutoPDL: Automatic Prompt Optimization for LLM Agents—0
AutoML Benchmark with shorter time constraints and early stopping—0
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Exploring Visual Complaints through a test battery in Acquired Brain Injury Patients: A Detailed Analysis of the DiaNAH Dataset—0
COPA: Comparing the Incomparable to Explore the Pareto Front—0
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak LearnersCode0
AutoQML: A Framework for Automated Quantum Machine LearningCode0
Online Meta-learning for AutoML in Real-time (OnMAR)—0
AutoML for Multi-Class Anomaly Compensation of Sensor DriftCode0
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction—0
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through OptionsCode0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation—0
Modeling All Response Surfaces in One for Conditional Search Spaces—0
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation—0
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data—0
Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals—0
Extreme AutoML: Analysis of Classification, Regression, and NLP Performance—0
Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systems—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