AutoML
236 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Libraries
Use these libraries to find AutoML models and implementations
NAS-Bench-101
MedMNIST
Chalearn-AutoML-1
Latest papers
auto-sktime: Automated Time Series Forecasting
The framework employs Bayesian optimization, to automatically construct pipelines from statistical, machine learning (ML) and deep neural network (DNN) models.
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML
AutoML platforms have numerous options for the algorithms to try for each step of the analysis, i. e., different possible algorithms for imputation, transformations, feature selection, and modelling.
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers
While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines.
A knowledge-driven AutoML architecture
The main goal is to render the AutoML process explainable and to leverage domain knowledge in the synthesis of pipelines and features.
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
We introduce TabRepo, a new dataset of tabular model evaluations and predictions.
Clairvoyance: A Pipeline Toolkit for Medical Time Series
vanderschaarlab/clairvoyance
•
•
ICLR 2021
Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.
Embedding in Recommender Systems: A Survey
applied-machine-learning-lab/embedding-in-recommender-systems
•
•
This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
This observation enables us to extend a variety of HPO and NAS algorithms to solve the Auto-FP problem.
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule
As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests.
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking
The proposed approach's feasibility is demonstrated by speeding up the state-of-the-art AutoML system on a synthetic data set with no performance loss.
