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
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoMLCode0
Learning Activation Functions for Sparse Neural NetworksCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak LearnersCode0
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture SearchCode0
A Robust Experimental Evaluation of Automated Multi-Label Classification MethodsCode0
Automatic and effective discovery of quantum kernelsCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML ApproachCode0
Exploring Robust Architectures for Deep Artificial Neural NetworksCode0
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical EvolutionCode0
Evolutionary Architecture Search for Graph Neural NetworksCode0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for CzechiaCode0
Benchmark and Survey of Automated Machine Learning FrameworksCode0
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoMLCode0
Encoding high-cardinality string categorical variablesCode0
Manas: Mining Software Repositories to Assist AutoMLCode0
Automated Imbalanced LearningCode0
Rafiki: Machine Learning as an Analytics Service SystemCode0
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution PruningCode0
Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During FormationCode0
Efficient Non-Parametric Optimizer Search for Diverse TasksCode0
Efficient Deep Learning Board: Training Feedback Is Not All You NeedCode0
AQMLator -- An Auto Quantum Machine Learning E-PlatformCode0
Tabular Data: Deep Learning is Not All You NeedCode0
Automated Machine Learning: From Principles to PracticesCode0
Efficient Automatic Machine Learning via Design GraphsCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation StudyCode0
Meta-learning of textual representationsCode0
Adaptation Strategies for Automated Machine Learning on Evolving DataCode0
X Hacking: The Threat of Misguided AutoMLCode0
Efficient and Joint Hyperparameter and Architecture Search for Collaborative FilteringCode0
Do We Really Need Imputation in AutoML Predictive Modeling?Code0
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular DataCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine LearningCode0
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained AnalysisCode0
Confidence Interval Estimation of Predictive Performance in the Context of AutoMLCode0
Auto-Keras: An Efficient Neural Architecture Search SystemCode0
ML-Plan: Automated machine learning via hierarchical planningCode0
Comparison of Automated Machine Learning Tools for SMS Spam Message FilteringCode0
Clairvoyance: A Pipeline Toolkit for Medical Time SeriesCode0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
Towards Model Selection using Learning Curve Cross-ValidationCode0
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular DataCode0
SAFE ML: Surrogate Assisted Feature Extraction for Model LearningCode0
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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