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 426–450 of 641 papers

TitleStatusHype
Privileged Zero-Shot AutoML—0
Problem-oriented AutoML in Clustering—0
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning—0
PyGlove: Symbolic Programming for Automated Machine Learning—0
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML—0
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning—0
Real-Time AutoML—0
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML—0
Replacing the Ex-Def Baseline in AutoML by Naive AutoML—0
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
Reusing Trained Layers of Convolutional Neural Networks to Shorten Hyperparameters Tuning Time—0
Review of automated time series forecasting pipelines—0
Revisiting Neural Architecture Search—0
RF+clust for Leave-One-Problem-Out Performance Prediction—0
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning—0
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions—0
Scalable End-to-End ML Platforms: from AutoML to Self-serve—0
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure—0
SEAL: Searching Expandable Architectures for Incremental Learning—0
Search-based Methods for Multi-Cloud Configuration—0
Searching to Exploit Memorization Effect in Learning with Noisy Labels—0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning—0
Selecting Optimal Trace Clustering Pipelines with AutoML—0
Semantic-Based Neural Network Repair—0
Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation—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