Assoz. Prof. Dipl.-Ing. Dr.techn. Sebastian Tschiatschek, BSc
1090 Wien
Room : 4.40
Courses
Summer term 2026
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052323 VU Probabilistic Artificial Intelligence
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053021 LP Practical Course: Computer Science 1
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053031 LP Practical Course: Computer Science 2
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053049 SE Master Seminar
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053621 VU Mining Massive Data
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053631 LP Data Analysis Project
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053640 SE Master's Seminar
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500501 SE Doctoral Research Seminar - Data and Knowledge
Winter term 2025
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051032 VU Foundations of Intelligent Systems
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053021 LP Practical Course: Computer Science 1
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053031 LP Practical Course: Computer Science 2
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053049 SE Master Seminar
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053613 VU Introduction to Machine Learning
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053631 LP Data Analysis Project
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500501 SE Doctoral Research Seminar - Data and Knowledge
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960028 VU Basics of IT and Data Science
Summer term 2025
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052323 VU Probabilistic Artificial Intelligence
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053021 LP Practical Course: Computer Science 1
-
053031 LP Practical Course: Computer Science 2
-
053049 SE Master Seminar
-
053621 VU Mining Massive Data
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053631 LP Data Analysis Project
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053640 SE Master's Seminar
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500501 SE Doctoral Research Seminar - Data and Knowledge
Publications
Ghosh, A, Tschiatschek, S, Devlin, S & Singla, A 2022, Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. in MM Rodrigo, N Matsuda, AI Cristea & V Dimitrova (eds), Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I. Springer, Cham, Lecture Notes in Computer Science, vol. 13355, pp. 28-40, Artificial Intelligence in Education (AIED), Durham, United Kingdom, 27/07/22. https://doi.org/10.1007/978-3-031-11644-5_3
Tschiatschek, S & Han, D 2022, Option Transfer and SMDP Abstraction with Successor Features. in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. pp. 3036-3042, IJCAI: International Joint Conference on Artificial Intelligence, Wien, Austria, 23/07/22. https://doi.org/10.24963/ijcai.2022/421
Lindner, D, Tschiatschek, S, Hofmann, K & Krause, A 2022, Interactively Learning Preference Constraints in Linear Bandits. in Proceedings of the 39th International Conference on Machine Learning: 17-23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research, vol. 162, International Conference on Machine Learning (ICML), Baltimore, United States, 17/07/22. <https://proceedings.mlr.press/v162/lindner22a.html>
Tschiatschek, S, Knobelsdorf, M & Singla, A 2022, Equity and Fairness of Bayesian Knowledge Tracing. in Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022. https://doi.org/10.5281/zenodo.6853012
Han, D, Wooldridge, M & Tschiatschek, S 2022, 'MDP Abstraction with Successor Features', Paper presented at AAAI-22 Workshop on Reinforcement Learning in Games, 28/02/22.
Han, D, Wooldridge, M & Tschiatschek, S 2021 'MDP Abstraction with Successor Features' arXiv.org. <https://arxiv.org/abs/2110.09196>
Yin, H, Chen, J, Pan , SJ & Tschiatschek, S 2021, 'Sequential Generative Exploration Model for Partially Observable Reinforcement Learning', Proceedings of the ... National Conference on Artificial Intelligence, vol. 35, no. 12, pp. 10700-10708. <https://ojs.aaai.org/index.php/AAAI/article/view/17279>
Tschiatschek, S, Morrison, C, Cutrell, E, Grayson, M, Thieme, A, Taylor, AS, Roumen, G, Longden, C, Marques, RF & Sellen, A 2021, Social Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child. in CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems., 396, Association for Computing Machinery (ACM), pp. 1-14. https://doi.org/10.1145/3411764.3445290
Tschiatschek, S, Lamb, A, Saveliev, E, Li, Y, Longden, C, Woodhead, S, Hernandez-Lobato, JM, Turner, R, Cameron, P & Zhang, C 2021 'Contextual HyperNetworks for Novel Feature Adaptation' arXiv.org. <https://arxiv.org/abs/2104.05860>
Tschiatschek, S, Lindner, D, Turchetta, M, Ciosek, K & Krause, A 2021 'Information Directed Reward Learning for Reinforcement Learning' arXiv.org. <https://arxiv.org/abs/2102.12466>
Wang, Z, Tschiatschek, S, Woodhead, S, Hernandez-Lobato, JM, Jones , SP, Baraniuk , RG & Zhang, C 2021, Educational Question Mining At Scale: Prediction, Analysis and Personalization. in Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, AAAI Press, pp. 15669-15677, Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), 2/02/21. <https://ojs.aaai.org/index.php/AAAI/article/view/17846>
Miklautz, L, Bauer, L, Mautz, D, Tschiatschek, S, Böhm, C & Plant, C 2021, Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces. in Z-H Zhou (ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21): Montreal, 19-27 August 2021. International Joint Conferences on Artificial Intelligence, pp. 2826-2832, IJCAI International Joint Conference on Artificial Intelligence 2021, Montreal, Canada, 19/08/21. https://doi.org/10.24963/ijcai.2021/389
Yin, H, Li, Y, Pan , SJ, Zhang, C & Tschiatschek, S 2020 'Reinforcement Learning with Efficient Active Feature Acquisition' arXiv.org.
Chien, I, Enrique, A, Palacios, J, Regan, T, Keegan, D, Carter, D, Tschiatschek, S, Nori, A, Thieme, A, Richards, D, Doherty, G & Belgrave, D 2020, 'A machine learning approach to understanding patterns of engagement with internet-delivered mental health interventions', JAMA Network Open, vol. 3, no. 7, e2010791. https://doi.org/10.1001/jamanetworkopen.2020.10791
Han, D, Wooldridge, M, Rogers, A, Tople, S, Ohrimenko, O & Tschiatschek, S 2020 'Replication-Robust Payoff-Allocation for Machine Learning Data Markets' arXiv.org.
Beck, J, Ciosek, K, Devlin, S, Tschiatschek, S, Zhang, C & Hofmann, K 2020, 'AMRL: Aggregated Memory For Reinforcement Learning'. <http://eprints.cs.univie.ac.at/6338/>
Ma, C, Tschiatschek, S, Hernandez-Lobato, JM, Turner, R & Zhang, C 2020, 'VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data'. <https://openreview.net/pdf?id=JZ-6j-siNBj>
Ghosh, A, Tschiatschek, S, Mahdavi, H & Singla, A 2020, 'Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies', Paper presented at AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems, Auckland, New Zealand, 9/05/20 - 13/05/20 pp. 447-455.
Roth, W, Schindler, G, Zöhrer, M, Pfeifenberger, L, Peharz, R, Tschiatschek, S, Fröning, H, Pernkopf, F & Ghahramani, Z 2020 'Resource-Efficient Neural Networks for Embedded Systems' arXiv.org.
Ma, C, Tschiatschek, S, Li, Y, Turner, R, Hernandez-Lobato, JM & Zhang, C 2020, 'HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals', pp. 1-8. <http://eprints.cs.univie.ac.at/6337/>
Projects
Interpretability and Explainability as Drivers to Democracy
Tschiatschek, S. (Project Lead), Möller, T. (Co-Lead) & Coeckelbergh, M. (Co-Lead)
1/10/21 → 30/09/26
Project: Research funding
Talks
Considering Respondents’ Preferences: The Effects of Self-Selecting the Content in Web Survey Questionnaires
Pfaff, K. (Speaker), Kritzinger, S. (Contributor), Tschiatschek, S. (Contributor) & Weitzel, D. (Contributor)
Activity: Talks and presentations › Talk or oral contribution › Science to Science
Automated Split Questionnaire Design: The Way Forward in Survey Research?
Pfaff, K. (Contributor), Kritzinger, S. (Speaker), Tschiatschek, S. (Contributor) & Weitzel, D. (Contributor)
Activity: Talks and presentations › Talk or oral contribution › Science to Science
Automated Split Questionnaire Design: The Way Forward in Survey Research?
Pfaff, K. (Speaker), Kritzinger, S. (Contributor), Tschiatschek, S. (Contributor) & Weitzel, D. (Contributor)
Activity: Talks and presentations › Talk or oral contribution › Science to Science
Machine Learning for Neural Imaging
Tschiatschek, S. (Speaker)
Activity: Talks and presentations › Talk or oral contribution › Science to Science