Prof. Dr. rer. nat. Tobias Huber

BEISSEL, Patrick, Tobias HUBER, Martin NOWAK, Elisabeth ANDRÉ und Stefan KÜNZELL, 2026. Choosing difficulty: Self-determined versus assigned tasks in motor sequence learning. Human Movement Science, 2026(107), 103483. ISSN 1872-7646. Verfügbar unter: https://doi.org/10.1016/j.humov.2026.103483

SÜMER, Ömer, Tobias HUBER, Dat DUONG, Suzanna E. LEDGISTER HANCHARD, Cristina CONATI, Elisabeth ANDRÉ, Benjamin D. SOLOMON und Rebekah L. WAIKEL, 2025. Evaluation of a Deep Learning and XAI based Facial Phenotyping Tool for Genetic Syndromes: A Clinical User Study [Preprint]. medRxiv. Verfügbar unter: https://doi.org/10.1101/2025.06.08.25328588

SCHÜTT, Anan, Tobias HUBER, Jauwairia NASIR, Cristina CONATI und Elisabeth ANDRÉ, 2024. Does Difficulty even Matter? Investigating Difficulty Adjustment and Practice Behavior in an Open-Ended Learning Task. Proceedings of the 14th Learning Analytics and Knowledge Conference. New York: ACM, S. 253-262. ISBN 979-8-4007-1618-8. Verfügbar unter: https://doi.org/10.1145/3636555.3636876

SCHÜTT, Anan, Tobias HUBER und Elisabeth ANDRÉ, 2024. Estimating Chess Puzzle Difficulty Without Past Game Records Using a Human Problem-Solving Inspired Neural Network Architecture. In: DING, Wei, Chang-Tien LU, Fusheng WANG, Liping DI, Kesheng WU, Jun HUAN, Raghu NAMBIAR, Jundong LI, Filip ILIEVSKI, Ricardo BAEZA-YATES und Xiaohua HU, Hrsg. Proceedings: 2024 IEEE International Conference on Big Data. Piscataway: IEEE, S. 8396-8402. ISBN 979-8-3503-6248-0. Verfügbar unter: https://doi.org/10.1109/BigData62323.2024.10826087

SCHLAGOWSKI, Ruben, Frederick HERGET, Niklas HEIMERL, Maximilian HAMMERL, Tobias HUBER, Pamina ZWOLSKY, Jan GRUCA und Elisabeth ANDRÉ, 2024. From a Social POV: The Impact of Point of View on Player Behavior, Engagement, and Experience in a Serious Social Simulation Game. In: SMITH, Gillian, Jim WHITEHEAD, Ben SAMUEL, Katta SPIEL und Riemer VAN ROZEN, Hrsg. Proceedings of the 19th International Conference on the Foundations of Digital Games, FDG 2024. New York: ACM. ISBN 979-8-4007-0955-5. Verfügbar unter: https://doi.org/10.1145/3649921.3649936

MERTES, Silvan, Tobias HUBER, Christina KARLE, Katharina WEITZ, Ruben SCHLAGOWSKI, Cristina CONATI und Elisabeth ANDRÉ, 2024. Relevant Irrelevance: Generating Alterfactual Explanations for Image Classifiers. In: LARSON, Kate, Hrsg. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, (IJCAI-24). Wien: IJCAI, S. 467-475. ISBN 978-1-956792-04-1. Verfügbar unter: https://doi.org/10.24963/ijcai.2024/52

WEBER, Klaus, Lukas TINNES, Tobias HUBER und Elisabeth ANDRÉ, 2023. Exploring the Effect of Visual-Based Subliminal Persuasion in Public Speeches Using Explainable AI Techniques. In: DEGEN, Helmut und Stavroula NTOA, Hrsg. Artificial Intelligence in HCI: 4th International Conference, AI-HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part I. Cham: Springer, S. 381-397. ISBN 978-3-031-35891-3. Verfügbar unter: https://doi.org/10.1007/978-3-031-35891-3_23

SCHÜTT, Anan, Tobias HUBER, Ilhan ASLAN und Elisabeth ANDRÉ, 2023. Fast Dynamic Difficulty Adjustment for Intelligent Tutoring Systems with Small Datasets. In: FENG, Mingyu, Tanja KÄSER und Partha TALUKDAR, Hrsg. Proceedings of the 16th International Conference on Educational Data Mining. Worcester: IEDMS, S. 482-489. ISBN 978-1-7336736-4-8. Verfügbar unter: https://doi.org/10.5281/zenodo.8115740

HUBER, Tobias, Maximilian DEMMLER, Silvan MERTES, Matthew L. OLSON und Elisabeth ANDRÉ, 2023. GANterfactual-RL: Understanding Reinforcement Learning Agents’ Strategies through Visual Counterfactual Explanations. AAMAS ’23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. Richland: IFAAMAS, S. 1097-1106. ISBN 978-1-4503-9432-1. Verfügbar unter: https://www.southampton.ac.uk/~eg/AAMAS2023/forms/contents.htm#3E

SEPTON, Yael, Tobias HUBER, Elisabeth ANDRÉ und Ofra AMIR, 2023. Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents. In: MATHIEU, Philippe, Frank DIGNUM, Paulo NOVAIS und Fernando DE LA PRIETA, Hrsg. Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection: 21st International Conference, PAAMS 2023, Guimarães, Portugal, July 12–14, 2023, Proceedings. Cham: Springer, S. 320-332. ISBN 978-3-031-37616-0. Verfügbar unter: https://doi.org/10.1007/978-3-031-37616-0_27

MERTES, Silvan, Christina KARLE, Tobias HUBER, Katharina WEITZ, Ruben SCHLAGOWSKI und Elisabeth ANDRÉ, 2022. Alterfactual Explanations - The Relevance of Irrelevance for Explaining AI Systems [Preprint]. arXiv. Verfügbar unter: https://doi.org/10.48550/arXiv.2207.09374

HUBER, Tobias, Benedikt LIMMER und Elisabeth ANDRÉ, 2022. Benchmarking Perturbation-Based Saliency Maps for Explaining Atari Agents. Frontiers in Artificial Intelligence, 5, 903875. ISSN 2624-8212. Verfügbar unter: https://doi.org/10.3389/frai.2022.903875

PRAJOD, Pooja, Dominik SCHILLER, Tobias HUBER und Elisabeth ANDRÉ, 2022. Do Deep Neural Networks Forget Facial Action Units?—Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition. In: SHABAN-NEJAD, Arash, Martin MICHALOWSKI und Simone BIANCO, Hrsg. AI for Disease Surveillance and Pandemic Intelligence: Intelligent Disease Detection in Action. Cham: Springer, S. 217-233. ISBN 978-3-030-93080-6. Verfügbar unter: https://doi.org/10.1007/978-3-030-93080-6_16

MERTES, Silvan, Tobias HUBER, Katharina WEITZ, Alexander HEIMERL und Elisabeth ANDRÉ, 2022. GANterfactual—Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning. Frontiers in Artificial Intelligence, 5, 825565. ISSN 2624-8212. Verfügbar unter: https://doi.org/10.3389/frai.2022.825565

PRAJOD, Pooja, Tobias HUBER und Elisabeth ANDRÉ, 2022. Using Explainable AI to Identify Differences Between Clinical and Experimental Pain Detection Models Based on Facial Expressions. In: JÓNSSON, Björn Þór, Cathal GURRIN, Minh-Triet TRAN, Duc Tien DANG NGUYEN, Min-Chun HU, Huynh Thi Thanh BINH und Benoit HUET, Hrsg. MultiMedia Modeling: 28th International Conference, MMM 2022, Phu Quoc, Vietnam, June 6–10, 2022, Proceedings, Part I. Cham: Springer, S. 311-322. ISBN 978-3-030-98358-1. Verfügbar unter: https://doi.org/10.1007/978-3-030-98358-1_25

HUBER, Tobias, Silvan MERTES, Stanislava RANGELOVA, Simon FLUTURA und Elisabeth ANDRÉ, 2021. Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway: IEEE. ISBN 978-1-7281-9048-8. Verfügbar unter: https://doi.org/10.1109/SSCI50451.2021.9660086

HUBER, Tobias, Katharina WEITZ, Elisabeth ANDRÉ und Ofra AMIR, 2021. Local and global explanations of agent behavior: Integrating strategy summaries with saliency maps. Artificial Intelligence, 2021(301), 103571. ISSN 1872-7921. Verfügbar unter: https://doi.org/10.1016/j.artint.2021.103571

WEITZ, Katharina, Dominik SCHILLER, Ruben SCHLAGOWSKI, Tobias HUBER und Elisabeth ANDRÉ, 2020. “Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design. Journal on Multimodal User Interfaces, 15(2), 87-98. ISSN 1783-8738. Verfügbar unter: https://doi.org/10.1007/s12193-020-00332-0

FLUTURA, Simon, Andreas SEIDERER, Tobias HUBER, Katharina WEITZ, Ilhan ASLAN, Ruben SCHLAGOWSKI, Elisabeth ANDRÉ und Joachim RATHMANN, 2020. Interactive Machine Learning and Explainability in Mobile Classification of Forest-Aesthetics. Proceedings of the 6th EAI International Conference on Smart Objects and Technologies for Social Good. New York: ACM, S. 90-95. ISBN 978-1-4503-7559-7. Verfügbar unter: https://doi.org/10.1145/3411170.3411225

SCHILLER, Dominik, Tobias HUBER, Michael DIETZ und Elisabeth ANDRÉ, 2020. Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition. Frontiers in Computer Science, 2, 6. ISSN 2624-9898. Verfügbar unter: https://doi.org/10.3389/fcomp.2020.00006

WEBER, Klaus, Lukas TINNES, Tobias HUBER, Alexander HEIMERL, Marc-Leon REINECKER, Eva POHLEN und Elisabeth ANDRÉ, 2020. Towards Demystifying Subliminal Persuasiveness: Using XAI-Techniques to Highlight Persuasive Markers of Public Speeches. In: CALVARESI, Davide, Amro NAJJAR, Michael WINIKOFF und Kary FRÄMLING, Hrsg. Explainable, Transparent Autonomous Agents and Multi-Agent Systems: Second International Workshop, EXTRAAMAS 2020, Auckland, New Zealand, May 9–13, 2020, Revised Selected Papers. Cham: Springer, S. 113-128. ISBN 978-3-030-51924-7. Verfügbar unter: https://doi.org/10.1007/978-3-030-51924-7_7

WEITZ, Katharina, Dominik SCHILLER, Ruben SCHLAGOWSKI, Tobias HUBER und Elisabeth ANDRÉ, 2019. "Do you trust me?": Increasing User-Trust by Integrating Virtual Agents in Explainable AI Interaction Design. IVA’19: Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents. New York: ACM, S. 7-9. ISBN 978-1-4503-6672-4. Verfügbar unter: https://doi.org/10.1145/3308532.3329441

HUBER, Tobias, Dominik SCHILLER und Elisabeth ANDRÉ, 2019. Enhancing Explainability of Deep Reinforcement Learning Through Selective Layer-Wise Relevance Propagation. In: BENZMÜLLER, Christoph und Heiner STUCKENSCHMIDT, Hrsg. KI 2019: Advances in Artificial Intelligence, 42nd German Conference on AI, Kassel, Germany, September 23–26, 2019, Proceedings. Cham: Springer, S. 188-202. ISBN 978-3-030-30179-8. Verfügbar unter: https://doi.org/10.1007/978-3-030-30179-8_16

RANGELOVA, Stanislava, Simon FLUTURA, Tobias HUBER, Daniel MOTUS und Elisabeth ANDRÉ, 2019. Exploration of Physiological Signals Using Different Locomotion Techniques in a VR Adventure Game. In: ANTONA, Margherita und Constantine STEPHANIDIS, Hrsg. Universal Access in Human-Computer Interaction: Theory, Methods and Tools, 13th International Conference, UAHCI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part I. Cham: Springer, S. 601-616. ISBN 978-3-030-23560-4. Verfügbar unter: https://doi.org/10.1007/978-3-030-23560-4_44

SCHILLER, Dominik, Tobias HUBER, Florian LINGENFELSER, Michael DIETZ, Andreas SEIDERER und Elisabeth ANDRÉ, 2019. Relevance-based Feature Masking: Improving Neural Network based Whale Classification through Explainable Artificial Intelligence. Proceedings Interspeech 2019. Grenoble: ISCA, S. 2423-2427. Verfügbar unter: https://doi.org/10.21437/Interspeech.2019-2707