3 papers
cs.LG2019
Learning to Tune XGBoost with XGBoost
Johanna Sommer, Dimitrios Sarigiannis, Thomas Parnell
In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH…
cs.LG2019
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning
Dimitrios Sarigiannis, Thomas Parnell, Haris Pozidis
The combined algorithm selection and hyperparameter tuning (CASH) problem is characterized by large hierarchical hyperparameter spaces. Model-free hyperparameter tuning methods can…
cs.LG2018
Snap ML: A Hierarchical Framework for Machine Learning
Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis +5
We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learn…