Generalized transitivity: A systematic comparison of concepts with an application to preferences in the Babington Smith model. - Björn Haddenhorst; Eyke Hüllermeier; Martin Kolb (Int. J. Approx. Reason., 2020)

Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. - Björn Haddenhorst; Viktor Bengs; Eyke Hüllermeier (NeurIPS, 2021)

Testification of Condorcet Winners in dueling bandits. - Björn Haddenhorst; Viktor Bengs; Jasmin Brandt; Eyke Hüllermeier (UAI, 2021)

On testing transitivity in online preference learning. - Björn Haddenhorst; Viktor Bengs; Eyke Hüllermeier (Mach. Learn., 2021)

Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. - Jasmin Brandt; Viktor Bengs; Björn Haddenhorst; Eyke Hüllermeier (NeurIPS, 2022)

Learning context-dependent choice functions. - Karlson Pfannschmidt; Pritha Gupta; Björn Haddenhorst; Eyke Hüllermeier (Int. J. Approx. Reason., 2022)

AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. - Jasmin Brandt; Elias Schede; Björn Haddenhorst; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney (AAAI, 2023)

Testing coherence and identifying winners in dueling bandits: theory and algorithms - Björn Haddenhorst (2023)

Identifying Copeland Winners in Dueling Bandits with Indifferences. - Viktor Bengs; Björn Haddenhorst; Eyke Hüllermeier (AISTATS, 2024)