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 1–25 of 641 papers

TitleStatusHype
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
From Tiny Machine Learning to Tiny Deep Learning: A SurveyCode2
Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation—0
CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction—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
MLZero: A Multi-Agent System for End-to-end Machine Learning AutomationCode3
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
CAPO: Cost-Aware Prompt OptimizationCode2
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures—0
LEMUR Neural Network Dataset: Towards Seamless AutoMLCode1
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
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe SystemsCode7
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting—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