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Found 5 papers, 1 papers with code
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning
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.
The Combinatorial Multi-Bandit Problem and its Application to Energy Management
For our energy management application we propose a range of algorithms that combine exploration principles for multi-arm bandits with mathematical programming.
HAMLET -- A Learning Curve-Enabled Multi-Armed Bandit for Algorithm Selection
Automated algorithm selection and hyperparameter tuning facilitates the application of machine learning.
A Study of the Learning Progress in Neural Architecture Search Techniques
As a surprising result, we find that the learning curves are completely flat, i. e., there is no observable progress of the controller in terms of the performance of its generated architectures.
On the Performance of Differential Evolution for Hyperparameter Tuning
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.
