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

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Showing 251–300 of 641 papers

TitleStatusHype
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
Automated Imbalanced LearningCode0
Neural Architectural Backdoors—0
Efficient Automatic Machine Learning via Design GraphsCode0
Extensible Proxy for Efficient NASCode1
Multi-Agent Automated Machine Learning—0
Sample-Then-Optimize Batch Neural Thompson SamplingCode1
AutoML for Climate Change: A Call to ActionCode1
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost ProxiesCode0
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI SolutionsCode1
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene—0
Efficient Non-Parametric Optimizer Search for Diverse TasksCode0
Automatic and effective discovery of quantum kernelsCode0
Industrial Data Science for Batch Manufacturing Processes—0
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat—0
Fraud Dataset Benchmark and ApplicationsCode2
A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters—0
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
Task Selection for AutoML System Evaluation—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine LearningCode0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round—0
AMLB: an AutoML BenchmarkCode2
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture—0
DC-BENCH: Dataset Condensation BenchmarkCode1
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AICode2
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System—0
The Impact of Feature Quantity on Recommendation Algorithm Performance: A Movielens-100K Case StudyCode0
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
FEATHERS: Federated Architecture and Hyperparameter Search—0
STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm ComparisonCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
AutoML Two-Sample TestCode1
Zero-Shot AutoML with Pretrained ModelsCode1
Exploring the Intersection between Neural Architecture Search and Continual Learning—0
AutoML-Based Drought Forecast with Meteorological Variables—0
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoMLCode1
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Automated machine learning: AI-driven decision making in business analytics—0
A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases—0
Warm-starting DARTS using meta-learning—0
Bi-level Alignment for Cross-Domain Crowd CountingCode1
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained AnalysisCode0
Efficient Automated Deep Learning for Time Series ForecastingCode4
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
The Roles and Modes of Human Interactions with Automated Machine Learning 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