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 51–75 of 641 papers

TitleStatusHype
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine LearningCode0
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics—0
Efficient Deep Learning Board: Training Feedback Is Not All You NeedCode0
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling—0
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques—0
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space ModelsCode1
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure—0
Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection—0
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language ModelsCode0
Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During FormationCode0
Mental Disorders Detection in the Era of Large Language Models—0
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLCode2
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications—0
CliMB: An AI-enabled Partner for Clinical Predictive ModelingCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
AQMLator -- An Auto Quantum Machine Learning E-PlatformCode0
Problem-oriented AutoML in Clustering—0
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data ImbalanceCode0
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning—0
Towards Automated Machine Learning Research—0
forester: A Tree-Based AutoML Tool in RCode2
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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