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 301–325 of 641 papers

TitleStatusHype
AutoML in The Wild: Obstacles, Workarounds, and Expectations—0
AutoML @ NeurIPS 2018 challenge: Design and Results—0
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text—0
AutoML Systems For Medical Imaging—0
AutoML to generate ensembles of deep neural networks—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
AutoPDL: Automatic Prompt Optimization for LLM Agents—0
AutoPINN: When AutoML Meets Physics-Informed Neural Networks—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
AutoQ: Automated Kernel-Wise Neural Network Quantization—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification—0
Autostacker: A Compositional Evolutionary Learning System—0
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System—0
Auto-survey Challenge—0
A Very Brief and Critical Discussion on AutoML—0
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications—0
Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
Bit-Mixer: Mixed-precision networks with runtime bit-width selection—0
Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms—0
Budget-aware Query Tuning: An AutoML Perspective—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