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 226–250 of 641 papers

TitleStatusHype
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction—0
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
10 Years of Fair Representations: Challenges and Opportunities—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations—0
Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML—0
Confidence Interval Estimation of Predictive Performance in the Context of AutoMLCode0
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML ApproachCode0
Are Large Language Models the New Interface for Data Pipelines?—0
Position: A Call to Action for a Human-Centered AutoML Paradigm—0
AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML—0
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
Using Combinatorial Optimization to Design a High quality LLM Solution—0
Hyperparameter Importance Analysis for Multi-Objective AutoMLCode0
Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications—0
Large Language Models Synergize with Automated Machine LearningCode0
M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values—0
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models—0
Do We Really Need Imputation in AutoML Predictive Modeling?Code0
Integrating Hyperparameter Search into Model-Free AutoML with Context-Free GrammarsCode0
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks—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