Iterative Deepening Hyperband

no code implementations • 1 Feb 2023 • Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier

Hyperparameter optimization (HPO) is concerned with the automated search for the most appropriate hyperparameter configuration (HPC) of a parameterized machine learning algorithm.

Hyperparameter Optimization

AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration

1 code implementation • 1 Dec 2022 • Jasmin Brandt, Elias Schede, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier, Kevin Tierney

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.

Multi-Armed Bandits

Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget

no code implementations • 9 Feb 2022 • Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier

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 number of times specified by an overall budget.

A Survey of Methods for Automated Algorithm Configuration

no code implementations • 3 Feb 2022 • Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney

We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry.