Sebastian Tschiatschek

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 76

Top Topics

Variational Inference

Reinforcement Learning

Bayesian Network Classifiers

Top Venues

Publications

Sebastian Tschiatschek, Eugenia Stamboliev, Timothée Schmude, Mark Coeckelbergh, Laura Koesten
Challenging the Human-in-the-loop in Algorithmic Decision-making. CoRR (2024)
Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek
Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions. CoRR (2024)
David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Learning Safety Constraints from Demonstrations with Unknown Rewards. AISTATS (2024)
Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek
Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making. CoRR (2023)
Timur Sudak, Sebastian Tschiatschek
Posterior Consistency for Missing Data in Variational Autoencoders. CoRR (2023)
Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek
On the Impact of Explanations on Understanding of Algorithmic Decision-Making. FAccT (2023)
David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Learning Safety Constraints from Demonstrations with Unknown Rewards. CoRR (2023)
Timur Sudak, Sebastian Tschiatschek
Posterior Consistency for Missing Data in Variational Autoencoders. ECML/PKDD (2) (2023)
Silvia Poletti, Alberto Testolin, Sebastian Tschiatschek
Learning Constraints From Human Stop-Feedback in Reinforcement Learning. AAMAS (2023)
Simon Rittel, Sebastian Tschiatschek
Specifying Prior Beliefs over DAGs in Deep Bayesian Causal Structure Learning. ECAI (2023)
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 (2023)
Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek
On the Impact of Explanations on Understanding of Algorithmic Decision-Making. CoRR (2023)
Timo Klein, Susanna Weinberger, Adish Singla, Sebastian Tschiatschek
Active Third-Person Imitation Learning. CoRR (2023)
Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin, Adish Singla
Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. CoRR (2023)
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) (2023)
David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Interactively Learning Preference Constraints in Linear Bandits. CoRR (2022)
David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Interactively Learning Preference Constraints in Linear Bandits. ICML (2022)
Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin, Adish Singla
Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. AIED (1) (2022)
Sebastian Tschiatschek, Maria Knobelsdorf, Adish Singla
Equity and Fairness of Bayesian Knowledge Tracing. EDM (2022)
Sebastian Tschiatschek, Maria Knobelsdorf, Adish Singla
Equity and Fairness of Bayesian Knowledge Tracing. CoRR (2022)
Dongge Han, Sebastian Tschiatschek
Option Transfer and SMDP Abstraction with Successor Features. IJCAI (2022)
Haiyan Yin, Jianda Chen, Sinno Jialin Pan, Sebastian Tschiatschek
Sequential Generative Exploration Model for Partially Observable Reinforcement Learning. AAAI (2021)
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)
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 (2021)
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)
David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause
Information Directed Reward Learning for Reinforcement Learning. NeurIPS (2021)
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)
David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause
Information Directed Reward Learning for Reinforcement Learning. CoRR (2021)
Dongge Han, Michael J. Wooldridge, Sebastian Tschiatschek
MDP Abstraction with Successor Features. CoRR (2021)
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)
Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek
Reinforcement Learning with Efficient Active Feature Acquisition. CoRR (2020)
Dongge Han, Shruti Tople, Alex Rogers, Michael J. Wooldridge, Olga Ohrimenko, Sebastian Tschiatschek
Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces. CoRR (2020)
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 (2020)
Ahana Ghosh, Sebastian Tschiatschek, Hamed Mahdavi, Adish Singla
Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies. AAMAS (2020)
Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann
AMRL: Aggregated Memory For Reinforcement Learning. ICLR (2020)
Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR (2020)
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 (2020)
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. NeurIPS (2019)
Ahana Ghosh, Sebastian Tschiatschek, Hamed Mahdavi, Adish Singla
Towards Deployment of Robust AI Agents for Human-Machine Partnerships. CoRR (2019)
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 (2019)
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)
Ruixue Liu, Advait Sarkar, Erin Solovey, Sebastian Tschiatschek
Evaluating Rule-based Programming and ReinforcementLearning for Personalising an Intelligent System. IUI Workshops (2019)
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)
Olga Ohrimenko, Shruti Tople, Sebastian Tschiatschek
Collaborative Machine Learning Markets with Data-Replication-Robust Payments. CoRR (2019)
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)
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 (2019)
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. CoRR (2019)
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)
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)
Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause
Differentiable Submodular Maximization. CoRR (2018)
Luis Haug, Sebastian Tschiatschek, Adish Singla
Teaching Inverse Reinforcement Learners via Features and Demonstrations. CoRR (2018)
Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause
Learning User Preferences to Incentivize Exploration in the Sharing Economy. AAAI (2018)
Wolfgang Roth, Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf
Hybrid generative-discriminative training of Gaussian mixture models. Pattern Recognit. Lett. 112 (2018)
David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek
Successor Uncertainties: exploration and uncertainty in temporal difference learning. CoRR (2018)
Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, Katja Hofmann
Variational Inference for Data-Efficient Model Learning in POMDPs. CoRR (2018)
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 (2018)
Sebastian Tschiatschek, Adish Singla, Manuel Gomez-Rodriguez, Arpit Merchant, Andreas Krause
Fake News Detection in Social Networks via Crowd Signals. WWW (Companion Volume) (2018)
Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause
Differentiable Submodular Maximization. IJCAI (2018)
Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf
Sum-Product Networks for Sequence Labeling. CoRR (2018)
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 (2018)
Luis Haug, Sebastian Tschiatschek, Adish Singla
Teaching Inverse Reinforcement Learners via Features and Demonstrations. NeurIPS (2018)
Junyao Zhao, Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
Improving Optimization-Based Approximate Inference by Clamping Variables. UAI (2017)
Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. CoRR (2017)
Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf
Frame and Segment Level Recurrent Neural Networks for Phone Classification. INTERSPEECH (2017)
Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause
Learning User Preferences to Incentivize Exploration in the Sharing Economy. CoRR (2017)
Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause
Coordinated Online Learning With Applications to Learning User Preferences. CoRR (2017)
Sebastian Tschiatschek, Adish Singla, Manuel Gomez-Rodriguez, Arpit Merchant, Andreas Krause
Detecting Fake News in Social Networks via Crowdsourcing. CoRR (2017)
Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. ICML (2017)
Sebastian Tschiatschek, Adish Singla, Andreas Krause
Selecting Sequences of Items via Submodular Maximization. AAAI (2017)
Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause
Cooperative Graphical Models. NIPS (2016)
Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf
Virtual Adversarial Training Applied to Neural Higher-Order Factors for Phone Classification. INTERSPEECH (2016)
Adish Singla, Sebastian Tschiatschek, Andreas Krause
Actively Learning Hemimetrics with Applications to Eliciting User Preferences. CoRR (2016)
Sebastian Tschiatschek, Josip Djolonga, Andreas Krause
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation. AISTATS (2016)
Adish Singla, Sebastian Tschiatschek, Andreas Krause
Actively Learning Hemimetrics with Applications to Eliciting User Preferences. ICML (2016)
Adish Singla, Sebastian Tschiatschek, Andreas Krause
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization. AAAI (2016)
Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
Variational Inference in Mixed Probabilistic Submodular Models. NIPS (2016)
Sebastian Tschiatschek, Franz Pernkopf
Parameter Learning of Bayesian Network Classifiers Under Computational Constraints. ECML/PKDD (1) (2015)
Christian Knoll, Michael Rath, Sebastian Tschiatschek, Franz Pernkopf
Message Scheduling Methods for Belief Propagation. ECML/PKDD (2) (2015)
Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos
On Theoretical Properties of Sum-Product Networks. AISTATS (2015)
Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf
Structured Regularizer for Neural Higher-Order Sequence Models. ECML/PKDD (1) (2015)
Adish Singla, Sebastian Tschiatschek, Andreas Krause
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization. CoRR (2015)
Sebastian Tschiatschek, Franz Pernkopf
On Bayesian Network Classifiers with Reduced Precision Parameters. IEEE Trans. Pattern Anal. Mach. Intell. 37 (4) (2015)
Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf
Neural higher-order factors in conditional random fields for phoneme classification. INTERSPEECH (2015)
Sebastian Tschiatschek, Karin Paul, Franz Pernkopf
Integer Bayesian Network Classifiers. ECML/PKDD (3) (2014)
Sebastian Tschiatschek, Rishabh K. Iyer, Haochen Wei, Jeff A. Bilmes
Learning Mixtures of Submodular Functions for Image Collection Summarization. NIPS (2014)
Sebastian Tschiatschek, Carlos Eduardo Cancino Chacón, Franz Pernkopf
Bounds for Bayesian network classifiers with reduced precision parameters. ICASSP (2013)
Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf
The Most Generative Maximum Margin Bayesian Networks. ICML (3) (2013)
Sebastian Tschiatschek, Franz Pernkopf
On the Asymptotic Optimality of Maximum Margin Bayesian Networks. AISTATS (2013)
Franz Pernkopf, Michael Wohlmayr, Sebastian Tschiatschek
Maximum Margin Bayesian Network Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 34 (3) (2012)
Sebastian Tschiatschek, Franz Pernkopf
Convex Combinations of Maximum Margin Bayesian Network Classifiers. ICPRAM (1) (2012)
Sebastian Tschiatschek, Peter Reinprecht, Manfred Mücke, Franz Pernkopf
Bayesian Network Classifiers with Reduced Precision Parameters. ECML/PKDD (1) (2012)
Sebastian Tschiatschek, Nikolaus Mutsam, Franz Pernkopf
Handling missing features in maximum margin Bayesian network classifiers. MLSP (2012)