| 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 | 298 | 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 | 272 | 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 | 233 | 2014 |
| 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 | 176 | 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 | 137 | 2018 |
| On theoretical properties of sum-product networks R Peharz, S Tschiatschek, F Pernkopf, P Domingos Artificial Intelligence and Statistics, 744-752, 2015 | 129 | 2015 |
| 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 | 96 | 2020 |
| Maximum margin Bayesian network classifiers F Pernkopf, M Wohlmayr, S Tschiatschek IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (3), 521-532, 2011 | 79 | 2011 |
| 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 | 71 | 2020 |
| Noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization A Singla, S Tschiatschek, A Krause Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 68 | 2016 |
| 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 | 65 | 2019 |
| Selecting sequences of items via submodular maximization S Tschiatschek, A Singla, A Krause Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 55 | 2017 |
| Teaching inverse reinforcement learners via features and demonstrations L Haug, S Tschiatschek, A Singla Advances in Neural Information Processing Systems 31, 2018 | 54 | 2018 |
| Differentiable submodular maximization S Tschiatschek, A Sahin, A Krause arXiv preprint arXiv:1803.01785, 2018 | 50 | 2018 |
| Resource-efficient neural networks for embedded systems W Roth, G Schindler, B Klein, R Peharz, S Tschiatschek, H Fröning, ... Journal of Machine Learning Research 25 (50), 1-51, 2024 | 48 | 2024 |
| 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 | 48 | 2019 |
| 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 | 48 | 2019 |
| On Bayesian Network Classifiers with Reduced Precision Parameters S Tschiatschek, F Pernkopf IEEE Transactions on Pattern Analysis & Machine Intelligence 4 (37), 774-785, 2015 | 48 | 2015 |
| Introduction to Probabilistic Graphical Models F Pernkopf, R Peharz, S Tschiatschek | 45* | |
| Social Sensemaking with AI: Designing an Open-ended AI experience with a Blind Child C Morrison, E Cutrell, M Grayson, A Thieme, A Taylor, G Roumen, ... Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021 | 37 | 2021 |
Franz PernkopfGraz University of Technology在 tugraz.at 的电子邮件经过验证
