Research papers
| Paper topic | Paper title |
|---|---|
| HPO and CASH | Efficient and robust automated machine learning |
| HPO and CASH | Efficient parameter selection for support vector machines in classification and regression via model-based global optimization |
| HPO and CASH | Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science |
| HPO and CASH | Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA |
| HPO and CASH | AutoML pipeline selection: Efficiently navigating the combinatorial space. |
| HPO and CASH | Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning |
| Automated Feature Engineering | SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks |
| Automated Feature Engineering | The autofeat Python Library for Automated Feature Engineering and Selection |
| Automated Feature Engineering | Autolearn - automated feature generation and selection |
| Automated Feature Engineering | Deep feature synthesis: Towards automating data science endeavors |
| Automated Feature Engineering | Explorekit: Automatic feature generation and selection |
| Automated Clustering | AutoClust: A Framework for Automated Clustering based on Cluster Validity Indices |
| Increasing Transparency and Controllability in Automated Machine Learning | ATMSeer: Increasing Transparency and Controllability in AutoML |
| Increasing Transparency and Controllability in AutoML | Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems |
| Increasing Transparency and Controllability in AutoML | AutoAIViz: opening the blackbox of automated artificial intelligence with conditional parallel coordinates |
| Increasing Transparency and Controllability in AutoML | PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines |