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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 576–600 of 641 papers

Title	Date	Tasks	Status	Hype
Efficient Neural Interaction Function Search for Collaborative Filtering	Jun 28, 2019	AutoMLCollaborative Filtering	CodeCode Available	0
Meta-learning of textual representations	Jun 21, 2019	AutoMLMeta-Learning	CodeCode Available	0
Automated Machine Learning: State-of-The-Art and Open Challenges	Jun 5, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	0
Approximation capability of neural networks on spaces of probability measures and tree-structured domains	Jun 3, 2019	AutoML	—Unverified	0
Automated Machine Learning with Monte-Carlo Tree Search	Jun 1, 2019	AutoMLBayesian Optimization	CodeCode Available	0
Transferable AutoML by Model Sharing Over Grouped Datasets	Jun 1, 2019	AutoMLBIG-bench Machine Learning	—Unverified	0
Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit	May 31, 2019	AutoML	—Unverified	0
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning	May 28, 2019	AutoMLGPU	CodeCode Available	0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization	May 27, 2019	AutoMLimage-classification	CodeCode Available	0
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar	May 24, 2019	AutoMLBayesian Optimization	—Unverified	0
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development	May 22, 2019	AutoMLBayesian Optimization	CodeCode Available	0
Analysis of the AutoML Challenge Series 2015–2018	May 18, 2019	AutoMLBIG-bench Machine Learning	—Unverified	0
Towards Automatically-Tuned Deep Neural Networks	May 18, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	2
AutoDispNet: Improving Disparity Estimation With AutoML	May 17, 2019	AutoMLBayesian Optimization	CodeCode Available	0
AM-LFS: AutoML for Loss Function Search	May 17, 2019	AutoML	CodeCode Available	0
An ADMM Based Framework for AutoML Pipeline Configuration	May 1, 2019	AutoMLBinary Classification	—Unverified	0
Approximation capability of neural networks on sets of probability measures and tree-structured data	May 1, 2019	AutoML	—Unverified	0
Benchmark and Survey of Automated Machine Learning Frameworks	Apr 26, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	0
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding	Apr 26, 2019	AutoMLGraph Embedding	CodeCode Available	1
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning	Apr 26, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	0
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification	Apr 23, 2019	AutoMLGeneral Classification	—Unverified	0
Neural Architecture Search for Deep Face Recognition	Apr 21, 2019	AutoMLFace Recognition	—Unverified	0
Adaptive Bayesian Linear Regression for Automated Machine Learning	Apr 1, 2019	AutoMLBayesian Optimization	—Unverified	0
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning	Mar 25, 2019	AutoMLMeta-Learning	CodeCode Available	1
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML	Mar 20, 2019	AutoMLContinual Learning	—Unverified	0

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All datasets Chalearn-AutoML-1 Breast Cancer Coimbra Data Set OrdinalDataset Wine

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	marc.boulle	Rank (AutoML5)	6.4	—	Unverified
2	reference_mb	Rank (AutoML5)	5.2	—	Unverified
3	postech.mlg_exbrain	Rank (AutoML5)	5.2	—	Unverified
4	abhishek4	Rank (AutoML5)	4.6	—	Unverified
5	reference	Rank (AutoML5)	4.4	—	Unverified
6	reference_ls	Rank (AutoML5)	4	—	Unverified
7	djajetic	Rank (AutoML5)	3	—	Unverified
8	aad_freiburg	Rank (AutoML5)	1.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Logistic Regression	Accuracy	97.02	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Zero-shot-BERT-SORT	1:1 Accuracy	55	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Logistic Regression	accuracy	98.33	—	Unverified