136 citations · 141 across the 7 of their papers we have counts for
7 papers
Adaptive Resampling with Bootstrap for Noisy Multi-Objective Optimization Problems
Timo Budszuhn, Mark Joachim Krallmann, Daniel Horn
The challenge of noisy multi-objective optimization lies in the constant trade-off between exploring new decision points and improving the precision of known points through resampl…
RODD: Robust Outlier Detection in Data Cubes
Lara Kuhlmann, Daniel Wilmes, Emmanuel Müller +2
Data cubes are multidimensional databases, often built from several separate databases, that serve as flexible basis for data analysis. Surprisingly, outlier detection on data cube…
Using Sequential Statistical Tests for Efficient Hyperparameter Tuning
Philip Buczak, Andreas Groll, Markus Pauly +2
Hyperparameter tuning is one of the the most time-consuming parts in machine learning. Despite the existence of modern optimization algorithms that minimize the number of evaluatio…
Random boosting and random^2 forests -- A random tree depth injection approach
Tobias Markus Krabel, Thi Ngoc Tien Tran, Andreas Groll +2
The induction of additional randomness in parallel and sequential ensemble methods has proven to be worthwhile in many aspects. In this manuscript, we propose and examine a novel r…
A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces
Martin Zaefferer, Daniel Horn
Many real-world optimization problems require significant resources for objective function evaluations. This is a challenge to evolutionary algorithms, as it limits the number of a…
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
Bernd Bischl, Jakob Richter, Jakob Bossek +3
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-bo…