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 551–600 of 641 papers

TitleStatusHype
How Powerful are Performance Predictors in Neural Architecture Search?—0
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI—0
Human-Centered AI for Data Science: A Systematic Approach—0
Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism—0
Hyperparameters in Reinforcement Learning and How To Tune Them—0
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study—0
HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks—0
Impact of HPO on AutoML Forecasting Ensembles—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
Improving generalisation of AutoML systems with dynamic fitness evaluations—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Incorporating domain knowledge into neural-guided search—0
Incorporating domain knowledge into neural-guided search via in situ priors and constraints—0
Incremental Search Space Construction for Machine Learning Pipeline Synthesis—0
Industrial Data Science for Batch Manufacturing Processes—0
Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines—0
Interpret-able feedback for AutoML systems—0
Iterative Compression of End-to-End ASR Model using AutoML—0
JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization—0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms—0
Joint Search of Data Augmentation Policies and Network Architectures—0
Katib: A Distributed General AutoML Platform on Kubernetes—0
KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI—0
Large Language Model Agent for Hyper-Parameter Optimization—0
Learning to Be A Doctor: Searching for Effective Medical Agent Architectures—0
Lessons learned from the AutoML challenge—0
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance—0
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection—0
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility—0
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Man versus Machine: AutoML and Human Experts' Role in Phishing Detection—0
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques—0
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat—0
M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values—0
Melatect: A Machine Learning Model Approach For Identifying Malignant Melanoma in Skin Growths—0
Memory-efficient Embedding for Recommendations—0
Mental Disorders Detection in the Era of Large Language Models—0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round—0
Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection—0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
Metalearning Using Structure-rich Pipeline Representations for Better AutoML—0
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning—0
Mining Robust Default Configurations for Resource-constrained AutoML—0
MLOps -- Definitions, Tools and Challenges—0
Model Evaluation for Domain Identification of Unknown Classes in Open-World Recognition: A Proposal—0
Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data—0
Modeling All Response Surfaces in One for Conditional Search Spaces—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