AutoML
237 papers with code • 2 benchmarks • 7 datasets
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
Benchmarks
These leaderboards are used to track progress in AutoML
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Libraries
Use these libraries to find AutoML models and implementations
NAS-Bench-101
MedMNIST
Chalearn-AutoML-1
Most implemented papers
GAMA: a General Automated Machine learning Assistant
The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoML research itself.
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018.
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
We present MedMNIST, a collection of 10 pre-processed medical open datasets.
Rethinking Neural Operations for Diverse Tasks
mkhodak/relax
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NeurIPS 2021
An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains.
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also an important problem in the literature.
Bilinear Scoring Function Search for Knowledge Graph Learning
We first set up a search space for AutoBLM by analyzing existing scoring functions.
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
sberbank-ai-lab/lightautoml
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We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions.
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.
AutoML Two-Sample Test
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
