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 151200 of 641 papers

TitleStatusHype
Direct Differentiable Augmentation SearchCode1
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?Code1
DriveML: An R Package for Driverless Machine LearningCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality MeasurementsCode1
Bi-level Alignment for Cross-Domain Crowd CountingCode1
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoMLCode1
MetaPoison: Practical General-purpose Clean-label Data PoisoningCode1
MixConv: Mixed Depthwise Convolutional KernelsCode1
Multi-Objective Evolutionary Design of Composite Data-Driven ModelsCode1
PFNs4BO: In-Context Learning for Bayesian OptimizationCode1
Efficient Hyper-parameter Search for Knowledge Graph EmbeddingCode1
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature GradientCode1
AutoDC: Automated data-centric processingCode1
A Robust Experimental Evaluation of Automated Multi-Label Classification MethodsCode0
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoMLCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
AMDet: A Tool for Mitotic Cell Detection in Histopathology SlidesCode0
Large Language Models Synergize with Automated Machine LearningCode0
Automated Machine Learning: State-of-The-Art and Open ChallengesCode0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
Automated Machine Learning with Monte-Carlo Tree SearchCode0
AQMLator -- An Auto Quantum Machine Learning E-PlatformCode0
Katib: A Distributed General AutoML Platform on KubernetesCode0
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Learning Activation Functions for Sparse Neural NetworksCode0
Manas: Mining Software Repositories to Assist AutoMLCode0
Integrating Hyperparameter Search into Model-Free AutoML with Context-Free GrammarsCode0
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation StudyCode0
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Information Leakage Detection through Approximate Bayes-optimal PredictionCode0
Automated Imbalanced LearningCode0
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous EnvironmentCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
Improve Deep Forest with Learnable Layerwise Augmentation Policy ScheduleCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine LearningCode0
Imbalanced Regression Pipeline RecommendationCode0
Auto-Keras: An Efficient Neural Architecture Search SystemCode0
Benchmarking AutoML algorithms on a collection of synthetic classification problemsCode0
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning PipelinesCode0
Hyperparameters in Reinforcement Learning and How To Tune ThemCode0
A knowledge-driven AutoML architectureCode0
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-LearnCode0
Hyperparameter Importance Analysis for Multi-Objective AutoMLCode0
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation ToolCode0
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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