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ORCIDSebastian Tschiatschek
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- affiliation: University of Vienna, Austria
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2020 – today
- 2024
[j5]Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani:
Resource-Efficient Neural Networks for Embedded Systems. J. Mach. Learn. Res. 25: 50:1-50:51 (2024)- [c51]David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Learning Safety Constraints from Demonstrations with Unknown Rewards. AISTATS 2024: 2386-2394 - [i37]Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek:
Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions. CoRR abs/2401.13324 (2024) - [i36]Sebastian Tschiatschek, Eugenia Stamboliev, Timothée Schmude, Mark Coeckelbergh, Laura Koesten:
Challenging the Human-in-the-loop in Algorithmic Decision-making. CoRR abs/2405.10706 (2024) - 2023
- [j4]Dongge Han
, Michael J. Wooldridge, Alex Rogers, Olga Ohrimenko, Sebastian Tschiatschek:
Replication Robust Payoff Allocation in Submodular Cooperative Games. IEEE Trans. Artif. Intell. 4(5): 1114-1128 (2023) - [c50]Silvia Poletti, Alberto Testolin, Sebastian Tschiatschek:
Learning Constraints From Human Stop-Feedback in Reinforcement Learning. AAMAS 2023: 2328-2330 - [c49]Simon Rittel, Sebastian Tschiatschek:
Specifying Prior Beliefs over DAGs in Deep Bayesian Causal Structure Learning. ECAI 2023: 1962-1969 - [c48]Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek:
On the Impact of Explanations on Understanding of Algorithmic Decision-Making. FAccT 2023: 959-970 - [c47]Timur Sudak, Sebastian Tschiatschek:
Posterior Consistency for Missing Data in Variational Autoencoders. ECML/PKDD (2) 2023: 508-524 - [e1]Gabriel Pedroza, Xiaowei Huang, Xin Cynthia Chen, Andreas Theodorou, Huáscar Espinoza, Nikolaos Matragkas, José Hernández-Orallo, Mauricio Castillo-Effen, Richard Mallah, John A. McDermid, David M. Bossens, Bettina Könighofer, Sebastian Tschiatschek, Anqi Liu:
Proceedings of the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL 2023) co-located with the 32nd International Joint Conference on Artificial Intelligence(IJCAI2023), Macau, China, August 21-22, 2023. CEUR Workshop Proceedings 3505, CEUR-WS.org 2023 [contents] - [i35]Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek:
On the Impact of Explanations on Understanding of Algorithmic Decision-Making. CoRR abs/2302.08264 (2023) - [i34]Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin, Adish Singla:
Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. CoRR abs/2303.16359 (2023) - [i33]David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Learning Safety Constraints from Demonstrations with Unknown Rewards. CoRR abs/2305.16147 (2023) - [i32]Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek:
Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making. CoRR abs/2305.16700 (2023) - [i31]Manh Hung Nguyen, Sebastian Tschiatschek, Adish Singla:
Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming from One-Shot Observation. CoRR abs/2310.10690 (2023) - [i30]Timur Sudak, Sebastian Tschiatschek:
Posterior Consistency for Missing Data in Variational Autoencoders. CoRR abs/2310.16648 (2023) - [i29]Timo Klein, Susanna Weinberger, Adish Singla, Sebastian Tschiatschek:
Active Third-Person Imitation Learning. CoRR abs/2312.16365 (2023) - 2022
- [c46]Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin, Adish Singla:
Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. AIED (1) 2022: 28-40 - [c45]Sebastian Tschiatschek, Maria Knobelsdorf, Adish Singla:
Equity and Fairness of Bayesian Knowledge Tracing. EDM 2022 - [c44]David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Interactively Learning Preference Constraints in Linear Bandits. ICML 2022: 13505-13527 - [c43]Dongge Han, Sebastian Tschiatschek:
Option Transfer and SMDP Abstraction with Successor Features. IJCAI 2022: 3036-3042 - [i28]Sebastian Tschiatschek, Maria Knobelsdorf, Adish Singla:
Equity and Fairness of Bayesian Knowledge Tracing. CoRR abs/2205.02333 (2022) - [i27]David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Interactively Learning Preference Constraints in Linear Bandits. CoRR abs/2206.05255 (2022) - 2021
- [c42]Haiyan Yin, Jianda Chen, Sinno Jialin Pan, Sebastian Tschiatschek:
Sequential Generative Exploration Model for Partially Observable Reinforcement Learning. AAAI 2021: 10700-10708 - [c41]Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang:
Educational Question Mining At Scale: Prediction, Analysis and Personalization. AAAI 2021: 15669-15677 - [c40]Cecily Morrison, Edward Cutrell, Martin Grayson, Anja Thieme, Alex S. Taylor, Geert Roumen, Camilla Longden, Sebastian Tschiatschek, Rita Faia Marques, Abigail Sellen:
Social Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child. CHI 2021: 396:1-396:14 - [c39]Lukas Miklautz, Lena G. M. Bauer, Dominik Mautz, Sebastian Tschiatschek, Christian Böhm, Claudia Plant:
Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces. IJCAI 2021: 2826-2832 - [c38]David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause:
Information Directed Reward Learning for Reinforcement Learning. NeurIPS 2021: 3850-3862 - [i26]David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause:
Information Directed Reward Learning for Reinforcement Learning. CoRR abs/2102.12466 (2021) - [i25]Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang:
Contextual HyperNetworks for Novel Feature Adaptation. CoRR abs/2104.05860 (2021) - [i24]Dongge Han, Michael J. Wooldridge, Sebastian Tschiatschek:
MDP Abstraction with Successor Features. CoRR abs/2110.09196 (2021) - 2020
- [c37]Ahana Ghosh, Sebastian Tschiatschek, Hamed Mahdavi, Adish Singla:
Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies. AAMAS 2020: 447-455 - [c36]Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann:
AMRL: Aggregated Memory For Reinforcement Learning. ICLR 2020 - [c35]Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS 2020 - [i23]Wolfgang Roth, Günther Schindler, Matthias Zöhrer, Lukas Pfeifenberger, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani:
Resource-Efficient Neural Networks for Embedded Systems. CoRR abs/2001.03048 (2020) - [i22]Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Cheng Zhang:
Large-Scale Educational Question Analysis with Partial Variational Auto-encoders. CoRR abs/2003.05980 (2020) - [i21]Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR abs/2006.11941 (2020) - [i20]Dongge Han, Shruti Tople, Alex Rogers, Michael J. Wooldridge, Olga Ohrimenko, Sebastian Tschiatschek:
Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces. CoRR abs/2006.14583 (2020) - [i19]Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek:
Reinforcement Learning with Efficient Active Feature Acquisition. CoRR abs/2011.00825 (2020)
2010 – 2019
- 2019
- [c34]Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI 2019: 1-8 - [c33]Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang:
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. ICML 2019: 4234-4243 - [c32]Ruixue Liu, Advait Sarkar, Erin Solovey, Sebastian Tschiatschek:
Evaluating Rule-based Programming and ReinforcementLearning for Personalising an Intelligent System. IUI Workshops 2019 - [c31]Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla:
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. NeurIPS 2019: 4147-4157 - [c30]David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek:
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. NeurIPS 2019: 4509-4518 - [c29]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. NeurIPS 2019: 13956-13968 - [c28]Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. NeurIPS 2019: 14791-14802 - [i18]Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla:
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. CoRR abs/1906.00429 (2019) - [i17]Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. CoRR abs/1908.04537 (2019) - [i16]Ahana Ghosh, Sebastian Tschiatschek, Hamed Mahdavi, Adish Singla:
Towards Deployment of Robust AI Agents for Human-Machine Partnerships. CoRR abs/1910.02330 (2019) - [i15]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. CoRR abs/1910.12911 (2019) - [i14]Olga Ohrimenko, Shruti Tople, Sebastian Tschiatschek:
Collaborative Machine Learning Markets with Data-Replication-Robust Payments. CoRR abs/1911.09052 (2019) - 2018
- [j3]Wolfgang Roth, Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf:
Hybrid generative-discriminative training of Gaussian mixture models. Pattern Recognit. Lett. 112: 131-137 (2018) - [c27]Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause:
Learning User Preferences to Incentivize Exploration in the Sharing Economy. AAAI 2018: 2248-2256 - [c26]Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause:
Differentiable Submodular Maximization. IJCAI 2018: 2731-2738 - [c25]Luis Haug, Sebastian Tschiatschek, Adish Singla:
Teaching Inverse Reinforcement Learners via Features and Demonstrations. NeurIPS 2018: 8473-8482 - [c24]Sebastian Tschiatschek, Adish Singla, Manuel Gomez-Rodriguez, Arpit Merchant, Andreas Krause:
Fake News Detection in Social Networks via Crowd Signals. WWW (Companion Volume) 2018: 517-524 - [i13]Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause:
Differentiable Submodular Maximization. CoRR abs/1803.01785 (2018) - [i12]Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, Katja Hofmann:
Variational Inference for Data-Efficient Model Learning in POMDPs. CoRR abs/1805.09281 (2018) - [i11]Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf:
Sum-Product Networks for Sequence Labeling. CoRR abs/1807.02324 (2018) - [i10]Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang:
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. CoRR abs/1809.11142 (2018) - [i9]David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek:
Successor Uncertainties: exploration and uncertainty in temporal difference learning. CoRR abs/1810.06530 (2018) - [i8]Luis Haug, Sebastian Tschiatschek, Adish Singla:
Teaching Inverse Reinforcement Learners via Features and Demonstrations. CoRR abs/1810.08926 (2018) - [i7]Franz Pernkopf, Wolfgang Roth, Matthias Zöhrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani:
Efficient and Robust Machine Learning for Real-World Systems. CoRR abs/1812.02240 (2018) - 2017
- [c23]Sebastian Tschiatschek, Adish Singla, Andreas Krause:
Selecting Sequences of Items via Submodular Maximization. AAAI 2017: 2667-2673 - [c22]Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek:
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. ICML 2017: 498-507 - [c21]Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf:
Frame and Segment Level Recurrent Neural Networks for Phone Classification. INTERSPEECH 2017: 1318-1322 - [c20]Junyao Zhao, Josip Djolonga, Sebastian Tschiatschek, Andreas Krause:
Improving Optimization-Based Approximate Inference by Clamping Variables. UAI 2017 - [i6]Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause:
Coordinated Online Learning With Applications to Learning User Preferences. CoRR abs/1702.02849 (2017) - [i5]Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek:
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. CoRR abs/1703.02100 (2017) - [i4]Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause:
Learning User Preferences to Incentivize Exploration in the Sharing Economy. CoRR abs/1711.08331 (2017) - [i3]Sebastian Tschiatschek, Adish Singla, Manuel Gomez-Rodriguez, Arpit Merchant, Andreas Krause:
Detecting Fake News in Social Networks via Crowdsourcing. CoRR abs/1711.09025 (2017) - 2016
- [c19]Adish Singla, Sebastian Tschiatschek, Andreas Krause:
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization. AAAI 2016: 2037-2043 - [c18]Sebastian Tschiatschek, Josip Djolonga, Andreas Krause:
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation. AISTATS 2016: 770-779 - [c17]Adish Singla, Sebastian Tschiatschek, Andreas Krause:
Actively Learning Hemimetrics with Applications to Eliciting User Preferences. ICML 2016: 412-420 - [c16]Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf:
Virtual Adversarial Training Applied to Neural Higher-Order Factors for Phone Classification. INTERSPEECH 2016: 2756-2760 - [c15]Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause:
Cooperative Graphical Models. NIPS 2016: 262-270 - [c14]Josip Djolonga, Sebastian Tschiatschek, Andreas Krause:
Variational Inference in Mixed Probabilistic Submodular Models. NIPS 2016: 1759-1767 - [i2]Adish Singla, Sebastian Tschiatschek, Andreas Krause:
Actively Learning Hemimetrics with Applications to Eliciting User Preferences. CoRR abs/1605.07144 (2016) - 2015
- [j2]Sebastian Tschiatschek, Franz Pernkopf:
On Bayesian Network Classifiers with Reduced Precision Parameters. IEEE Trans. Pattern Anal. Mach. Intell. 37(4): 774-785 (2015) - [c13]Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos:
On Theoretical Properties of Sum-Product Networks. AISTATS 2015 - [c12]Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf:
Neural higher-order factors in conditional random fields for phoneme classification. INTERSPEECH 2015: 2137-2141 - [c11]Sebastian Tschiatschek, Franz Pernkopf:
Parameter Learning of Bayesian Network Classifiers Under Computational Constraints. ECML/PKDD (1) 2015: 86-101 - [c10]Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf:
Structured Regularizer for Neural Higher-Order Sequence Models. ECML/PKDD (1) 2015: 168-183 - [c9]Christian Knoll, Michael Rath, Sebastian Tschiatschek, Franz Pernkopf:
Message Scheduling Methods for Belief Propagation. ECML/PKDD (2) 2015: 295-310 - [i1]Adish Singla, Sebastian Tschiatschek, Andreas Krause:
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization. CoRR abs/1511.07211 (2015) - 2014
- [c8]Sebastian Tschiatschek, Rishabh K. Iyer, Haochen Wei, Jeff A. Bilmes:
Learning Mixtures of Submodular Functions for Image Collection Summarization. NIPS 2014: 1413-1421 - [c7]Sebastian Tschiatschek, Karin Paul, Franz Pernkopf:
Integer Bayesian Network Classifiers. ECML/PKDD (3) 2014: 209-224 - 2013
- [c6]Sebastian Tschiatschek, Franz Pernkopf:
On the Asymptotic Optimality of Maximum Margin Bayesian Networks. AISTATS 2013: 590-598 - [c5]Sebastian Tschiatschek, Carlos Eduardo Cancino Chacón, Franz Pernkopf:
Bounds for Bayesian network classifiers with reduced precision parameters. ICASSP 2013: 3357-3361 - [c4]Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf:
The Most Generative Maximum Margin Bayesian Networks. ICML (3) 2013: 235-243 - 2012
- [j1]Franz Pernkopf, Michael Wohlmayr, Sebastian Tschiatschek:
Maximum Margin Bayesian Network Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 34(3): 521-532 (2012) - [c3]Sebastian Tschiatschek, Franz Pernkopf:
Convex Combinations of Maximum Margin Bayesian Network Classifiers. ICPRAM (1) 2012: 69-77 - [c2]Sebastian Tschiatschek, Nikolaus Mutsam, Franz Pernkopf:
Handling missing features in maximum margin Bayesian network classifiers. MLSP 2012: 1-6 - [c1]Sebastian Tschiatschek, Peter Reinprecht, Manfred Mücke, Franz Pernkopf:
Bayesian Network Classifiers with Reduced Precision Parameters. ECML/PKDD (1) 2012: 74-89
Coauthor Index
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