5 citations · 7 across the 4 of their papers we have counts for
4 papers
Identifying Copeland Winners in Dueling Bandits with Indifferences
Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
We consider the task of identifying the Copeland winner(s) in a dueling bandits problem with ternary feedback. This is an underexplored but practically relevant variant of the conv…
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…
Learning Context-Dependent Choice Functions
Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst +1
Choice functions accept a set of alternatives as input and produce a preferred subset of these alternatives as output. We study the problem of learning such functions under conditi…