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 551–575 of 641 papers

TitleStatusHype
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoMLCode0
Learning Activation Functions for Sparse Neural NetworksCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak LearnersCode0
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture SearchCode0
A Robust Experimental Evaluation of Automated Multi-Label Classification MethodsCode0
Automatic and effective discovery of quantum kernelsCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML ApproachCode0
Exploring Robust Architectures for Deep Artificial Neural NetworksCode0
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical EvolutionCode0
Evolutionary Architecture Search for Graph Neural NetworksCode0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for CzechiaCode0
Benchmark and Survey of Automated Machine Learning FrameworksCode0
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoMLCode0
Encoding high-cardinality string categorical variablesCode0
Manas: Mining Software Repositories to Assist AutoMLCode0
Automated Imbalanced LearningCode0
Rafiki: Machine Learning as an Analytics Service SystemCode0
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution PruningCode0
Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During FormationCode0
Efficient Non-Parametric Optimizer Search for Diverse TasksCode0
Efficient Deep Learning Board: Training Feedback Is Not All You NeedCode0
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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