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2020 – today
- 2024
[c5]Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Identifying Copeland Winners in Dueling Bandits with Indifferences. AISTATS 2024: 226-234- 2023
- [b1]Björn Haddenhorst:
Testing coherence and identifying winners in dueling bandits: theory and algorithms. Paderborn University, Germany, 2023 - [c4]Jasmin Brandt, Elias Schede, Björn Haddenhorst
, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. AAAI 2023: 12355-12363 - [i3]Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Identifying Copeland Winners in Dueling Bandits with Indifferences. CoRR abs/2310.00750 (2023) - 2022
- [j3]Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst, Eyke Hüllermeier:
Learning context-dependent choice functions. Int. J. Approx. Reason. 140: 116-155 (2022) - [c3]Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. NeurIPS 2022 - [i2]Jasmin Brandt, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. CoRR abs/2202.04487 (2022) - [i1]Jasmin Brandt, Elias Schede, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier, Kevin Tierney:
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. CoRR abs/2212.00333 (2022) - 2021
- [j2]Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
On testing transitivity in online preference learning. Mach. Learn. 110(8): 2063-2084 (2021) - [c2]Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. NeurIPS 2021: 25904-25916 - [c1]Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier:
Testification of Condorcet Winners in dueling bandits. UAI 2021: 1195-1205 - 2020
- [j1]Björn Haddenhorst, Eyke Hüllermeier, Martin Kolb:
Generalized transitivity: A systematic comparison of concepts with an application to preferences in the Babington Smith model. Int. J. Approx. Reason. 119: 373-407 (2020)
Coauthor Index
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