no code implementations • 23 Apr 2021 • Julia Gastinger, Sébastien Nicolas, Dušica Stepić, Mischa Schmidt, Anett Schülke
The contribution of this work is twofold: (1) We introduce a collection of ensemble methods for time series forecasting to combine predictions from base models.
no code implementations • 30 Oct 2020 • Tobias Jacobs, Mischa Schmidt, Sébastien Nicolas, Anett Schülke
For our energy management application we propose a range of algorithms that combine exploration principles for multi-arm bandits with mathematical programming.
no code implementations • 30 Jan 2020 • Mischa Schmidt, Julia Gastinger, Sébastien Nicolas, Anett Schülke
Automated algorithm selection and hyperparameter tuning facilitates the application of machine learning.
1 code implementation • 15 Apr 2019 • Mischa Schmidt, Shahd Safarani, Julia Gastinger, Tobias Jacobs, Sebastien Nicolas, Anett Schülke
This empirical study involves a range of different machine learning algorithms and datasets with various characteristics to compare the performance of Differential Evolution with Sequential Model-based Algorithm Configuration (SMAC), a reference Bayesian Optimization approach.