Sebastian Tschiatschek

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Introduction to Probabilistic Graphical Models

Sebastian Tschiatschek, Franz Pernkopf, Robert Peharz, 2014 .

Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces pdf

Sebastian Tschiatschek, Alex Rogers, Shruti Tople, 2020, ArXiv.

Learning Mixtures of Submodular Functions for Image Collection Summarization

Rishabh K. Iyer, Sebastian Tschiatschek, Jeff A. Bilmes, 2014, NIPS.

Fake News Detection in Social Networks via Crowd Signals

Sebastian Tschiatschek, Adish Singla, Andreas Krause, 2017, WWW.

M L ] 5 M ar 2 01 8 Differentiable Submodular Maximization

Andreas Krause, Sebastian Tschiatschek, Aytunc Sahin, 2019 .

Social Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child

Sebastian Tschiatschek, Abigail Sellen, Edward Cutrell, 2021, CHI.

A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions

Jorge E. Palacios, Gavin Doherty, Tim Regan, 2020, JAMA network open.

Option Transfer and SMDP Abstraction with Successor Features

Sebastian Tschiatschek, Dongge Han, 2021, IJCAI.

Replication Robust Payoff Allocation in Submodular Cooperative Games

M. Wooldridge, A. Rogers, Sebastian Tschiatschek, 2020, 2006.14583.

Interactively Learning Preference Constraints in Linear Bandits

A. Krause, Sebastian Tschiatschek, Katja Hofmann, 2022, ICML.

Replication Robust Payoff Allocation in Submodular Cooperative Games

M. Wooldridge, A. Rogers, Sebastian Tschiatschek, 2020, IEEE Transactions on Artificial Intelligence.

Educational Question Mining At Scale: Prediction, Analysis and Personalization

Sebastian Tschiatschek, Zichao Wang, Richard G. Baraniuk, 2021, AAAI.

Equity and Fairness of Bayesian Knowledge Tracing

A. Singla, Sebastian Tschiatschek, Maria Knobelsdorf, 2022, ArXiv.

Resource-Efficient Neural Networks for Embedded Systems

Zoubin Ghahramani, F. Pernkopf, Robert Peharz, 2020, ArXiv.

Variational Inference for Data-Efficient Model Learning in POMDPs pdf

Katja Hofmann, Sebastian Tschiatschek, Kai Arulkumaran, 2018, ArXiv.

Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization pdf

Andreas Krause, Sebastian Tschiatschek, Adish Singla, 2015, AAAI.

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals

Sebastian Tschiatschek, Cheng Zhang, Richard E. Turner, 2019, AABI.

Successor Uncertainties: exploration and uncertainty in temporal difference learning

Sebastian Tschiatschek, David Janz, José Miguel Hernández-Lobato, 2018, NeurIPS.

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

Sam Devlin, Sebastian Tschiatschek, Katja Hofmann, 2019, NeurIPS.

AMRL: Aggregated Memory For Reinforcement Learning

Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, 2020, ICLR.

Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces

Sebastian Tschiatschek, Claudia Plant, Lukas Miklautz, 2021, IJCAI.

Sequential Generative Exploration Model for Partially Observable Reinforcement Learning

Sebastian Tschiatschek, Sinno Jialin Pan, Haiyan Yin, 2021, AAAI.

Selecting Sequences of Items via Submodular Maximization

Andreas Krause, Sebastian Tschiatschek, Adish Singla, 2017, AAAI.

Guarantees for Greedy Maximization of Non-submodular Functions with Applications pdf

Andreas Krause, Joachim M. Buhmann, Sebastian Tschiatschek, 2017, ICML.

On the Impact of Explanations on Understanding of Algorithmic Decision-Making

Sebastian Tschiatschek, Laura M. Koesten, T. Möller, 2023, arXiv.org.

Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes

A. Singla, Sam Devlin, Sebastian Tschiatschek, 2023, AIED.

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data pdf

Sebastian Tschiatschek, Cheng Zhang, Richard E. Turner, 2020, NeurIPS.

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

Sebastian Nowozin, Sebastian Tschiatschek, Cheng Zhang, 2018, ICML.

Information Directed Reward Learning for Reinforcement Learning

K. Ciosek, A. Krause, M. Turchetta, 2021, NeurIPS.

Learning Safety Constraints from Demonstrations with Unknown Rewards

A. Krause, Sebastian Tschiatschek, Katja Hofmann, 2023, ArXiv.

Contextual HyperNetworks for Novel Feature Adaptation pdf

Sebastian Tschiatschek, Richard E. Turner, Angus Lamb, 2021, ArXiv.

Large-Scale Educational Question Analysis with Partial Variational Auto-encoders pdf

Sebastian Tschiatschek, Zichao Wang, Simon Woodhead, 2020, ArXiv.

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model

Sebastian Nowozin, Sebastian Tschiatschek, Cheng Zhang, 2019, NeurIPS.

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model pdf

Sebastian Nowozin, Sebastian Tschiatschek, Cheng Zhang, 2019, ArXiv.

Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation

Andreas Krause, Sebastian Tschiatschek, Josip Djolonga, 2016, AISTATS.

Differentiable Submodular Maximization

Andreas Krause, Sebastian Tschiatschek, Aytunc Sahin, 2018, IJCAI.

Message Scheduling Methods for Belief Propagation

Sebastian Tschiatschek, Franz Pernkopf, Michael Rath, 2015, ECML/PKDD.

Collaborative Machine Learning Markets

Sebastian Tschiatschek, Shruti Tople, Olga Ohrimenko, 2020 .

Collaborative Machine Learning Markets with Data-Replication-Robust Payments

Sebastian Tschiatschek, Shruti Tople, O. Ohrimenko, 2019, ArXiv.

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

José Miguel Hernández-Lobato, S. Nowozin, Konstantina Palla, 2018, ICML.

On the Asymptotic Optimality of Maximum Margin Bayesian Networks

Sebastian Tschiatschek, Franz Pernkopf, F. Pernkopf, 2013, AISTATS.

Convex Combinations of Maximum Margin Bayesian Network Classifiers

Sebastian Tschiatschek, Franz Pernkopf, F. Pernkopf, 2012, ICPRAM.

Efficient and Robust Machine Learning for Real-World Systems pdf

Holger Fröning, Sebastian Tschiatschek, Franz Pernkopf, 2018, ArXiv.

Virtual Adversarial Training Applied to Neural Higher-Order Factors for Phone Classification

Sebastian Tschiatschek, Franz Pernkopf, Martin Ratajczak, 2016, INTERSPEECH.

Parameter Learning of Bayesian Network Classifiers Under Computational Constraints

Sebastian Tschiatschek, Franz Pernkopf, F. Pernkopf, 2015, ECML/PKDD.

MDP Abstraction with Successor Features pdf

Sebastian Tschiatschek, Michael Wooldridge, Dongge Han, 2021, ArXiv.

Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints

A. Singla, Sebastian Tschiatschek, R. Devidze, 2019, NeurIPS.

Teaching Inverse Reinforcement Learners via Features and Demonstrations

Sebastian Tschiatschek, Adish Singla, Luis Haug, 2018, NeurIPS.

Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies

Sebastian Tschiatschek, Adish Singla, Ahana Ghosh, 2020, AAMAS.

Towards Deployment of Robust AI Agents for Human-Machine Partnerships pdf

Sebastian Tschiatschek, Adish Singla, Ahana Ghosh, 2019, ArXiv.

Handling missing features in maximum margin Bayesian network classifiers

Sebastian Tschiatschek, Nikolaus Mutsam, Franz Pernkopf, 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

Variational Inference in Mixed Probabilistic Submodular Models

Andreas Krause, Sebastian Tschiatschek, Josip Djolonga, 2016, NIPS.

Improving Optimization-Based Approximate Inference by Clamping Variables

Andreas Krause, Sebastian Tschiatschek, Josip Djolonga, 2017, UAI.

Maximum Margin Bayesian Network Classifiers

Sebastian Tschiatschek, Franz Pernkopf, Michael Wohlmayr, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning User Preferences to Incentivize Exploration in the Sharing Economy pdf

Andreas Krause, Sebastian Tschiatschek, Adish Singla, 2017, AAAI.

Coordinated Online Learning With Applications to Learning User Preferences pdf

Andreas Krause, Sebastian Tschiatschek, Adish Singla, 2017, ArXiv.

Reinforcement Learning with Efficient Active Feature Acquisition pdf

Sebastian Tschiatschek, Cheng Zhang, Sinno Jialin Pan, 2020, ArXiv.

Quantum-classical truncated Newton method for high-dimensional energy landscapes

Sebastian Tschiatschek, Leonard Wossnig, Stefan Zohren, 2017, 1710.07063.

Learning Constraints From Human Stop-Feedback in Reinforcement Learning

Alberto Testolin, Sebastian Tschiatschek, S. Poletti, 2023, AAMAS.

Actively Learning Hemimetrics with Applications to Eliciting User Preferences pdf

Andreas Krause, Sebastian Tschiatschek, Adish Singla, 2016, ICML.

Integer Bayesian Network Classifiers

Sebastian Tschiatschek, Franz Pernkopf, Karin Paul, 2014, ECML/PKDD.

On Bayesian Network Classifiers with Reduced Precision Parameters

Sebastian Tschiatschek, Franz Pernkopf, F. Pernkopf, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming from One-Shot Observation

A. Singla, Sebastian Tschiatschek, Manh Hung Nguyen, 2023, ArXiv.

On Theoretical Properties of Sum-Product Networks

Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos, 2015, AISTATS.

On the Impact of Explanations on Understanding of Algorithmic Decision-Making

Sebastian Tschiatschek, T. Möller, Timothée Schmude, 2023, FAccT.

Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

Sebastian Tschiatschek, T. Möller, Timothée Schmude, 2023, ArXiv.