Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoG, 2021)

The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022. - Ryan W. Gardner; Gino Perrotta; Anvay Shah; Shivaram Kalyanakrishnan; Kevin A. Wang; Gregory Clark; Timo Bertram; Johannes Fürnkranz; Martin Müller 003; Brady P. Garrison; Prithviraj Dasgupta; Saeid Rezaei (NeurIPS (Competition and Demos), 2021)

A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoRR, 2021)

Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoG, 2022)

Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoRR, 2022)

Weighting Information Sets with Siamese Neural Networks in Reconnaissance Blind Chess. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoG, 2023)

Learning With Generalised Card Representations for "Magic: The Gathering". - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoG, 2024)

Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (KI, 2024)

Contrastive Learning of Preferences with a Contextual InfoNCE Loss. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoRR, 2024)

Neural Network-Based Information Set Weighting for Playing Reconnaissance Blind Chess. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (IEEE Trans. Games, 2024)

Deceptive Game Design? Investigating the Impact of Visual Card Style on Player Perception. - Leonie Kallabis; Timo Bertram; Florian Rupp (CoG, 2025)

UrzaGPT: LoRA-Tuned Large Language Models for Card Selection in Collectible Card Games. - Timo Bertram (CoRR, 2025)