Sebastian Tschiatschek
Sebastian Tschiatschek
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Cited by
Cited by
Fake news detection in social networks via crowd signals
S Tschiatschek, A Singla, M Gomez Rodriguez, A Merchant, A Krause
Companion proceedings of the the web conference 2018, 517-524, 2018
Guarantees for greedy maximization of non-submodular functions with applications
AA Bian, JM Buhmann, A Krause, S Tschiatschek
International conference on machine learning, 498-507, 2017
Learning mixtures of submodular functions for image collection summarization
S Tschiatschek, RK Iyer, H Wei, JA Bilmes
Advances in neural information processing systems 27, 2014
On theoretical properties of sum-product networks
R Peharz, S Tschiatschek, F Pernkopf, P Domingos
Artificial Intelligence and Statistics, 744-752, 2015
Generalization in reinforcement learning with selective noise injection and information bottleneck
M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann
Neural Information Processing (NeurIPS), 2019
Eddi: Efficient dynamic discovery of high-value information with partial vae
C Ma, S Tschiatschek, K Palla, JM Hernández-Lobato, S Nowozin, ...
arXiv preprint arXiv:1809.11142, 2018
Maximum margin Bayesian network classifiers
F Pernkopf, M Wohlmayr, S Tschiatschek
IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (3), 521-532, 2011
Noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization
A Singla, S Tschiatschek, A Krause
Thirtieth AAAI Conference on Artificial Intelligence, 2016
A machine learning approach to understanding patterns of engagement with internet-delivered mental health interventions
I Chien, A Enrique, J Palacios, T Regan, D Keegan, D Carter, ...
JAMA network open 3 (7), e2010791-e2010791, 2020
Differentiable submodular maximization
S Tschiatschek, A Sahin, A Krause
arXiv preprint arXiv:1803.01785, 2018
On Bayesian Network Classifiers with Reduced Precision Parameters
S Tschiatschek, F Pernkopf
IEEE Transactions on Pattern Analysis & Machine Intelligence 4 (37), 774-785, 2015
Selecting sequences of items via submodular maximization
S Tschiatschek, A Singla, A Krause
Thirty-First AAAI Conference on Artificial Intelligence, 2017
Teaching inverse reinforcement learners via features and demonstrations
L Haug, S Tschiatschek, A Singla
Advances in Neural Information Processing Systems 31, 2018
Successor uncertainties: exploration and uncertainty in temporal difference learning
D Janz, J Hron, P Mazur, K Hofmann, JM Hernández-Lobato, ...
Advances in Neural Information Processing Systems 32, 2019
Introduction to Probabilistic Graphical Models
F Pernkopf, R Peharz, S Tschiatschek
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
S Tschiatschek, A Gosh, L Haug, R Devidze, A Singla
Advances in Neural Information Processing Systems 32 6, 4122-4132, 2019
Resource-efficient neural networks for embedded systems
W Roth, G Schindler, M Zöhrer, L Pfeifenberger, R Peharz, S Tschiatschek, ...
arXiv preprint arXiv:2001.03048, 2020
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation
S Tschiatschek, J Djolonga, A Krause
Icebreaker: Element-wise efficient information acquisition with a bayesian deep latent gaussian model
W Gong, S Tschiatschek, S Nowozin, RE Turner, JM Hernández-Lobato, ...
Advances in neural information processing systems 32, 2019
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
C Ma, S Tschiatschek, JM Hernández-Lobato, R Turner, C Zhang
Advances in Neural Information Processing Systems, 2020
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