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

Showing 201225 of 641 papers

TitleStatusHype
A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning ToolsCode0
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation ToolCode0
Hyperparameter Importance Analysis for Multi-Objective AutoMLCode0
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-LearnCode0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine LearningCode0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular DataCode0
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
AutoRL Hyperparameter LandscapesCode0
AutoRec: An Automated Recommender SystemCode0
AutoQML: A Framework for Automated Quantum Machine LearningCode0
Google Vizier: A Service for Black-Box OptimizationCode0
Imbalanced Regression Pipeline RecommendationCode0
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through OptionsCode0
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoMLCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Adaptation Strategies for Automated Machine Learning on Evolving DataCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
AutoDispNet: Improving Disparity Estimation With AutoMLCode0
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture SearchCode0
AutoML-Zero: Evolving Machine Learning Algorithms From ScratchCode0
Exploring Robust Architectures for Deep Artificial Neural NetworksCode0
AutoML using Metadata Language EmbeddingsCode0
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML ApproachCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified