Convex Combinations of Maximum Margin Bayesian Network Classifiers. - Sebastian Tschiatschek; Franz Pernkopf (ICPRAM (1), 2012)

Handling missing features in maximum margin Bayesian network classifiers. - Sebastian Tschiatschek; Nikolaus Mutsam; Franz Pernkopf (MLSP, 2012)

Bayesian Network Classifiers with Reduced Precision Parameters. - Sebastian Tschiatschek; Peter Reinprecht; Manfred Mücke; Franz Pernkopf (ECML/PKDD (1), 2012)

Maximum Margin Bayesian Network Classifiers. - Franz Pernkopf; Michael Wohlmayr; Sebastian Tschiatschek (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

On the Asymptotic Optimality of Maximum Margin Bayesian Networks. - Sebastian Tschiatschek; Franz Pernkopf (AISTATS, 2013)

Bounds for Bayesian network classifiers with reduced precision parameters. - Sebastian Tschiatschek; Carlos Eduardo Cancino Chacón; Franz Pernkopf (ICASSP, 2013)

The Most Generative Maximum Margin Bayesian Networks. - Robert Peharz; Sebastian Tschiatschek; Franz Pernkopf (ICML (3), 2013)

Learning Mixtures of Submodular Functions for Image Collection Summarization. - Sebastian Tschiatschek; Rishabh K. Iyer; Haochen Wei; Jeff A. Bilmes (NIPS, 2014)

Integer Bayesian Network Classifiers. - Sebastian Tschiatschek; Karin Paul; Franz Pernkopf (ECML/PKDD (3), 2014)

On Theoretical Properties of Sum-Product Networks. - Robert Peharz; Sebastian Tschiatschek; Franz Pernkopf; Pedro M. Domingos (AISTATS, 2015)

Neural higher-order factors in conditional random fields for phoneme classification. - Martin Ratajczak; Sebastian Tschiatschek; Franz Pernkopf (INTERSPEECH, 2015)

Message Scheduling Methods for Belief Propagation. - Christian Knoll 002; Michael Rath 001; Sebastian Tschiatschek; Franz Pernkopf (ECML/PKDD (2), 2015)

Structured Regularizer for Neural Higher-Order Sequence Models. - Martin Ratajczak; Sebastian Tschiatschek; Franz Pernkopf (ECML/PKDD (1), 2015)

Parameter Learning of Bayesian Network Classifiers Under Computational Constraints. - Sebastian Tschiatschek; Franz Pernkopf (ECML/PKDD (1), 2015)

On Bayesian Network Classifiers with Reduced Precision Parameters. - Sebastian Tschiatschek; Franz Pernkopf (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization. - Adish Singla; Sebastian Tschiatschek; Andreas Krause 001 (AAAI, 2016)

Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation. - Sebastian Tschiatschek; Josip Djolonga; Andreas Krause 001 (AISTATS, 2016)

Actively Learning Hemimetrics with Applications to Eliciting User Preferences. - Adish Singla; Sebastian Tschiatschek; Andreas Krause 001 (ICML, 2016)

Virtual Adversarial Training Applied to Neural Higher-Order Factors for Phone Classification. - Martin Ratajczak; Sebastian Tschiatschek; Franz Pernkopf (INTERSPEECH, 2016)

Cooperative Graphical Models. - Josip Djolonga; Stefanie Jegelka; Sebastian Tschiatschek; Andreas Krause 001 (NIPS, 2016)

Variational Inference in Mixed Probabilistic Submodular Models. - Josip Djolonga; Sebastian Tschiatschek; Andreas Krause 001 (NIPS, 2016)

Selecting Sequences of Items via Submodular Maximization. - Sebastian Tschiatschek; Adish Singla; Andreas Krause 001 (AAAI, 2017)

Guarantees for Greedy Maximization of Non-submodular Functions with Applications. - Andrew An Bian; Joachim M. Buhmann; Andreas Krause 001; Sebastian Tschiatschek (ICML, 2017)

Frame and Segment Level Recurrent Neural Networks for Phone Classification. - Martin Ratajczak; Sebastian Tschiatschek; Franz Pernkopf (INTERSPEECH, 2017)

Improving Optimization-Based Approximate Inference by Clamping Variables. - Junyao Zhao 001; Josip Djolonga; Sebastian Tschiatschek; Andreas Krause 001 (UAI, 2017)

Coordinated Online Learning With Applications to Learning User Preferences. - Christoph Hirnschall; Adish Singla; Sebastian Tschiatschek; Andreas Krause 001 (CoRR, 2017)

Detecting Fake News in Social Networks via Crowdsourcing. - Sebastian Tschiatschek; Adish Singla; Manuel Gomez-Rodriguez; Arpit Merchant; Andreas Krause 001 (CoRR, 2017)

Learning User Preferences to Incentivize Exploration in the Sharing Economy. - Christoph Hirnschall; Adish Singla; Sebastian Tschiatschek; Andreas Krause 001 (AAAI, 2018)

Differentiable Submodular Maximization. - Sebastian Tschiatschek; Aytunc Sahin; Andreas Krause 001 (IJCAI, 2018)

Teaching Inverse Reinforcement Learners via Features and Demonstrations. - Luis Haug; Sebastian Tschiatschek; Adish Singla (NeurIPS, 2018)

Fake News Detection in Social Networks via Crowd Signals. - Sebastian Tschiatschek; Adish Singla; Manuel Gomez-Rodriguez; Arpit Merchant; Andreas Krause 001 (WWW (Companion Volume), 2018)

Variational Inference for Data-Efficient Model Learning in POMDPs. - Sebastian Tschiatschek; Kai Arulkumaran; Jan Stühmer; Katja Hofmann (CoRR, 2018)

Sum-Product Networks for Sequence Labeling. - Martin Ratajczak; Sebastian Tschiatschek; Franz Pernkopf (CoRR, 2018)

Successor Uncertainties: exploration and uncertainty in temporal difference learning. - David Janz; Jiri Hron; José Miguel Hernández-Lobato; Katja Hofmann; Sebastian Tschiatschek (CoRR, 2018)

Efficient and Robust Machine Learning for Real-World Systems. - Franz Pernkopf; Wolfgang Roth; Matthias Zöhrer; Lukas Pfeifenberger; Günther Schindler; Holger Fröning; Sebastian Tschiatschek; Robert Peharz; Matthew Mattina; Zoubin Ghahramani (CoRR, 2018)

Hybrid generative-discriminative training of Gaussian mixture models. - Wolfgang Roth; Robert Peharz; Sebastian Tschiatschek; Franz Pernkopf (Pattern Recognit. Lett., 2018)

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. - Chao Ma 019; Sebastian Tschiatschek; Yingzhen Li; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (AABI, 2019)

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. - Chao Ma 019; Sebastian Tschiatschek; Konstantina Palla; José Miguel Hernández-Lobato; Sebastian Nowozin; Cheng Zhang 005 (ICML, 2019)

Evaluating Rule-based Programming and ReinforcementLearning for Personalising an Intelligent System. - Ruixue Liu; Advait Sarkar; Erin Solovey; Sebastian Tschiatschek (IUI Workshops, 2019)

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. - Wenbo Gong 001; Sebastian Tschiatschek; Sebastian Nowozin; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (NeurIPS, 2019)

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. - Maximilian Igl; Kamil Ciosek; Yingzhen Li; Sebastian Tschiatschek; Cheng Zhang 005; Sam Devlin; Katja Hofmann (NeurIPS, 2019)

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. - David Janz; Jiri Hron; Przemyslaw Mazur; Katja Hofmann; José Miguel Hernández-Lobato; Sebastian Tschiatschek (NeurIPS, 2019)

Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. - Sebastian Tschiatschek; Ahana Ghosh; Luis Haug; Rati Devidze; Adish Singla (NeurIPS, 2019)

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. - Wenbo Gong 001; Sebastian Tschiatschek; Richard E. Turner; Sebastian Nowozin; José Miguel Hernández-Lobato; Cheng Zhang 005 (CoRR, 2019)

Towards Deployment of Robust AI Agents for Human-Machine Partnerships. - Ahana Ghosh; Sebastian Tschiatschek; Hamed Mahdavi; Adish Singla (CoRR, 2019)

Collaborative Machine Learning Markets with Data-Replication-Robust Payments. - Olga Ohrimenko; Shruti Tople; Sebastian Tschiatschek (CoRR, 2019)

Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies. - Ahana Ghosh; Sebastian Tschiatschek; Hamed Mahdavi; Adish Singla (AAMAS, 2020)

AMRL: Aggregated Memory For Reinforcement Learning. - Jacob Beck; Kamil Ciosek; Sam Devlin; Sebastian Tschiatschek; Cheng Zhang 005; Katja Hofmann (ICLR, 2020)

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. - Chao Ma 019; Sebastian Tschiatschek; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (NeurIPS, 2020)

Resource-Efficient Neural Networks for Embedded Systems. - Wolfgang Roth; Günther Schindler; Matthias Zöhrer; Lukas Pfeifenberger; Robert Peharz; Sebastian Tschiatschek; Holger Fröning; Franz Pernkopf; Zoubin Ghahramani (CoRR, 2020)

Large-Scale Educational Question Analysis with Partial Variational Auto-encoders. - Zichao Wang 001; Sebastian Tschiatschek; Simon Woodhead 002; José Miguel Hernández-Lobato; Simon Peyton Jones; Cheng Zhang 005 (CoRR, 2020)

Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces. - Dongge Han; Shruti Tople; Alex Rogers; Michael J. Wooldridge; Olga Ohrimenko; Sebastian Tschiatschek (CoRR, 2020)

Reinforcement Learning with Efficient Active Feature Acquisition. - Haiyan Yin; Yingzhen Li; Sinno Jialin Pan; Cheng Zhang 005; Sebastian Tschiatschek (CoRR, 2020)

Educational Question Mining At Scale: Prediction, Analysis and Personalization. - Zichao Wang 001; Sebastian Tschiatschek; Simon Woodhead 002; José Miguel Hernández-Lobato; Simon Peyton Jones; Richard G. Baraniuk; Cheng Zhang 005 (AAAI, 2021)

Sequential Generative Exploration Model for Partially Observable Reinforcement Learning. - Haiyan Yin; Jianda Chen; Sinno Jialin Pan; Sebastian Tschiatschek (AAAI, 2021)

Social Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child. - Cecily Morrison; Edward Cutrell; Martin Grayson; Anja Thieme; Alex S. Taylor; Geert Roumen; Camilla Longden; Sebastian Tschiatschek; Rita Faia Marques; Abigail Sellen (CHI, 2021)

Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces. - Lukas Miklautz; Lena G. M. Bauer; Dominik Mautz; Sebastian Tschiatschek; Christian Böhm 001; Claudia Plant (IJCAI, 2021)

Information Directed Reward Learning for Reinforcement Learning. - David Lindner; Matteo Turchetta; Sebastian Tschiatschek; Kamil Ciosek; Andreas Krause 001 (NeurIPS, 2021)

Contextual HyperNetworks for Novel Feature Adaptation. - Angus Lamb; Evgeny Saveliev; Yingzhen Li; Sebastian Tschiatschek; Camilla Longden; Simon Woodhead 002; José Miguel Hernández-Lobato; Richard E. Turner; Pashmina Cameron; Cheng Zhang 005 (CoRR, 2021)

MDP Abstraction with Successor Features. - Dongge Han; Michael J. Wooldridge; Sebastian Tschiatschek (CoRR, 2021)

Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. - Ahana Ghosh; Sebastian Tschiatschek; Sam Devlin; Adish Singla (AIED (1), 2022)

Equity and Fairness of Bayesian Knowledge Tracing. - Sebastian Tschiatschek; Maria Knobelsdorf; Adish Singla (EDM, 2022)

Interactively Learning Preference Constraints in Linear Bandits. - David Lindner; Sebastian Tschiatschek; Katja Hofmann; Andreas Krause 001 (ICML, 2022)

Option Transfer and SMDP Abstraction with Successor Features. - Dongge Han; Sebastian Tschiatschek (IJCAI, 2022)

Learning Constraints From Human Stop-Feedback in Reinforcement Learning. - Silvia Poletti; Alberto Testolin; Sebastian Tschiatschek (AAMAS, 2023)

Specifying Prior Beliefs over DAGs in Deep Bayesian Causal Structure Learning. - Simon Rittel; Sebastian Tschiatschek (ECAI, 2023)

On the Impact of Explanations on Understanding of Algorithmic Decision-Making. - Timothée Schmude; Laura Koesten; Torsten Möller; Sebastian Tschiatschek (FAccT, 2023)

Posterior Consistency for Missing Data in Variational Autoencoders. - Timur Sudak; Sebastian Tschiatschek (ECML/PKDD (2), 2023)

Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making. - Timothée Schmude; Laura Koesten; Torsten Möller; Sebastian Tschiatschek (CoRR, 2023)

Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming from One-Shot Observation. - Manh Hung Nguyen; Sebastian Tschiatschek; Adish Singla (CoRR, 2023)

Active Third-Person Imitation Learning. - Timo Klein; Susanna Weinberger; Adish Singla; Sebastian Tschiatschek (CoRR, 2023)

Replication Robust Payoff Allocation in Submodular Cooperative Games. - Dongge Han; Michael J. Wooldridge; Alex Rogers; Olga Ohrimenko; Sebastian Tschiatschek (IEEE Trans. Artif. Intell., 2023)

Learning Safety Constraints from Demonstrations with Unknown Rewards. - David Lindner; Xin Chen; Sebastian Tschiatschek; Katja Hofmann; Andreas Krause 001 (AISTATS, 2024)

Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming. - Manh Hung Nguyen; Sebastian Tschiatschek; Adish Singla (EDM, 2024)

Challenging the Human-in-the-loop in Algorithmic Decision-making. - Sebastian Tschiatschek; Eugenia Stamboliev; Timothée Schmude; Mark Coeckelbergh; Laura Koesten (CoRR, 2024)

Plasticity Loss in Deep Reinforcement Learning: A Survey. - Timo Klein; Lukas Miklautz; Kevin Sidak; Claudia Plant; Sebastian Tschiatschek (CoRR, 2024)

Deliberative XAI: How Explanations Impact Understanding and Decision-Making of AI Novices in Collective and Individual Settings. - Timothée Schmude; Laura Koesten; Torsten Möller; Sebastian Tschiatschek (CoRR, 2024)

Resource-Efficient Neural Networks for Embedded Systems. - Wolfgang Roth; Günther Schindler; Bernhard Klein; Robert Peharz; Sebastian Tschiatschek; Holger Fröning; Franz Pernkopf; Zoubin Ghahramani (J. Mach. Learn. Res., 2024)

Synthesizing High-Quality Programming Tasks with LLM-Based Expert and Student Agents. - Manh Hung Nguyen; Victor-Alexandru Padurean; Alkis Gotovos; Sebastian Tschiatschek; Adish Singla (AIED (1), 2025)

Breaking the Reclustering Barrier in Centroid-based Deep Clustering. - Lukas Miklautz; Timo Klein; Kevin Sidak; Collin Leiber; Thomas Lang; Andrii Shkabrii; Sebastian Tschiatschek; Claudia Plant (ICLR, 2025)

On Constant Regret for Low-Rank MDPs. - Alexander Sturm; Sebastian Tschiatschek (UAI, 2025)

Information that matters: Exploring information needs of people affected by algorithmic decisions. - Timothée Schmude; Laura Koesten; Torsten Möller; Sebastian Tschiatschek (Int. J. Hum. Comput. Stud., 2025)

Rule-Guided Reinforcement Learning Policy Evaluation and Improvement. - Martin Tappler; Ignacio D. Lopez-Miguel; Sebastian Tschiatschek; Ezio Bartocci (CoRR, 2025)