65 citations · 68 across the 7 of their papers we have counts for
4 papers · 1 filter
Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
Leona Hennig, Jasmin Brandt, Lukas Fehring +3
Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and en…
Iterative Deepening Hyperband
Jasmin Brandt, Marcel Wever, Dimitrios Iliadis +2
Hyperparameter optimization (HPO) is concerned with the automated search for the most appropriate hyperparameter configuration (HPC) of a parameterized machine learning algorithm.…
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Jasmin Brandt, Elias Schede, Viktor Bengs +3
We study the algorithm configuration (AC) problem, in which one seeks to find an optimal parameter configuration of a given target algorithm in an automated way. Recently, there ha…
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget
Jasmin Brandt, Viktor Bengs, Björn Haddenhorst +1
We consider the combinatorial bandits problem with semi-bandit feedback under finite sampling budget constraints, in which the learner can carry out its action only for a limited n…