Publications

For an up-to-date list of my publications, please see my google scholar page.

2022

Han, D., Wooldridge, M., Rogers, A., Ohrimenko, O., & Tschiatschek, S. (2022). Replication robust payoff allocation in submodular cooperative games. IEEE Transactions on Artificial Intelligence (IEEE TAI).

@article{han2022replication,
  title = {Replication robust payoff allocation in submodular cooperative games},
  author = {Han, Dongge and Wooldridge, Michael and Rogers, Alex and Ohrimenko, Olga and Tschiatschek, Sebastian},
  journal = {IEEE Transactions on Artificial Intelligence (IEEE TAI)},
  year = {2022},
  publisher = {IEEE}
}

Lindner, D., Tschiatschek, S., Hofmann, K., & Krause, A. (2022). Interactively Learning Preference Constraints in Linear Bandits. International Conference on Machine Learning (ICML), 13505–13527.

@inproceedings{lindner2022interactively,
  title = {Interactively Learning Preference Constraints in Linear Bandits},
  author = {Lindner, David and Tschiatschek, Sebastian and Hofmann, Katja and Krause, Andreas},
  booktitle = {International Conference on Machine Learning (ICML)},
  pages = {13505--13527},
  year = {2022}
}

Ghosh, A., Tschiatschek, S., Devlin, S., & Singla, A. (2022). Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes.

@inproceedings{ghosh2022adaptive,
  title = {Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes},
  author = {Ghosh, Ahana and Tschiatschek, Sebastian and Devlin, Sam and Singla, Adish},
  year = {2022},
  organization = {International Conference on Artificial Intelligence in Education (AIED)}
}

Han, D., & Tschiatschek, S. (2022). Option Transfer and SMDP Abstraction with Successor Features. International Joint Conference on Artificial Intelligence (IJCAI).

@inproceedings{Han2022SMDP,
  title = {Option Transfer and SMDP Abstraction with Successor Features},
  author = {Han, Dongge and Tschiatschek, Sebastian},
  year = {2022},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)}
}

Tschiatschek, S., Knobelsdorf, M., & Singla, A. (2022). Equity and Fairness of Bayesian Knowledge Tracing. Educational Data Mining (EDM).

@inproceedings{Tschiatschek2022Fairness,
  title = {Equity and Fairness of Bayesian Knowledge Tracing},
  author = {Tschiatschek, Sebastian and Knobelsdorf, Maria and Singla, Adish},
  year = {2022},
  booktitle = {Educational Data Mining (EDM)}
}

2021

Lindner, D., Turchetta, M., Tschiatschek, S., Ciosek, K., & Krause, A. (2021). Information Directed Reward Learning for Reinforcement Learning. Advances in Neural Information Processing Systems.

@article{lindner2021information,
  title = {Information Directed Reward Learning for Reinforcement Learning},
  author = {Lindner, David and Turchetta, Matteo and Tschiatschek, Sebastian and Ciosek, Kamil and Krause, Andreas},
  journal = {Advances in Neural Information Processing Systems},
  year = {2021}
}

Miklautz, L., Bauer, L. G. M., Mautz, D., Tschiatschek, S., Böhm, C., & Plant, C. (2021). Details (Don’t) Matter: Isolating Cluster Information in Deep Embedded Spaces. International Joint Conference on Artificial Intelligence (IJCAI), 2826–2832.

@inproceedings{miklautz2021details,
  title = {Details (Don’t) Matter: Isolating Cluster Information in Deep Embedded Spaces},
  author = {Miklautz, Lukas and Bauer, Lena GM and Mautz, Dominik and Tschiatschek, Sebastian and B{\"o}hm, Christian and Plant, Claudia},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
  pages = {2826--2832},
  year = {2021}
}

Morrison, C., Cutrell, E., Grayson, M., Thieme, A., Taylor, A., Roumen, G., Longden, C., Tschiatschek, S., Faia Marques, R., & Sellen, A. (2021). Social Sensemaking with AI: Designing an Open-ended AI experience with a Blind Child. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–14.

@inproceedings{morrison2021social,
  title = {Social Sensemaking with AI: Designing an Open-ended AI experience with a Blind Child},
  author = {Morrison, Cecily and Cutrell, Edward and Grayson, Martin and Thieme, Anja and Taylor, Alex and Roumen, Geert and Longden, Camilla and Tschiatschek, Sebastian and Faia Marques, Rita and Sellen, Abigail},
  booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
  pages = {1--14},
  year = {2021}
}

Lamb, A., Saveliev, E., Li, Y., Tschiatschek, S., Longden, C., Woodhead, S., Hernández-Lobato, J. M., Turner, R. E., Cameron, P., & Zhang, C. (2021). Contextual HyperNetworks for Novel Feature Adaptation. In arXiv preprint arXiv:2104.05860.

@misc{lamb2021contextual,
  title = {Contextual HyperNetworks for Novel Feature Adaptation},
  author = {Lamb, Angus and Saveliev, Evgeny and Li, Yingzhen and Tschiatschek, Sebastian and Longden, Camilla and Woodhead, Simon and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel and Turner, Richard E and Cameron, Pashmina and Zhang, Cheng},
  journal = {arXiv preprint arXiv:2104.05860},
  year = {2021}
}

Yin, H., Chen, J., Pan, S. J., & Tschiatschek, S. (2021). Sequential Generative Exploration Model for Partially Observable Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 10700–10708.

@inproceedings{yin2021sequential,
  title = {Sequential Generative Exploration Model for Partially Observable Reinforcement Learning},
  author = {Yin, Haiyan and Chen, Jianda and Pan, Sinno Jialin and Tschiatschek, Sebastian},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {35},
  number = {12},
  pages = {10700--10708},
  year = {2021}
}

2020

Han, D., Tople, S., Rogers, A., Wooldridge, M., Ohrimenko, O., & Tschiatschek, S. (2020). Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces. https://arxiv.org/abs/2006.14583

@misc{Han2020ReplicationRobust,
  title = {Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces},
  author = {Han, Dongge and Tople, Shruti and Rogers, Alex and Wooldridge, Michael and Ohrimenko, Olga and Tschiatschek, Sebastian},
  year = {2020},
  eprint = {2006.14583},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/abs/2006.14583}
}

Ma, C., Tschiatschek, S., Hernández-Lobato, J. M., Turner, R., & Zhang, C. (2020). VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. https://arxiv.org/pdf/2006.11941.pdf

@misc{Ma2020VAEM,
  title = {VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data},
  author = {Ma, Chao and Tschiatschek, Sebastian and Hernández-Lobato, José Miguel and Turner, Richard and Zhang, Cheng},
  year = {2020},
  eprint = {2006.11941},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/pdf/2006.11941.pdf},
  teaserimage = {figures/pmi/ma2020VAEM.png},
  teasercaption = {Our VAEM accurately generates marginals and pairwise-marginals of real-world datasets},
  tag = {PMI,SDM}
}

Ghosh, A., Tschiatschek, S., Mahdavi, H., & Singla, A. (2020). Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies. International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). http://ifaamas.org/Proceedings/aamas2020/pdfs/p447.pdf

@inproceedings{ghosh2019deployment,
  title = {Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies},
  author = {Ghosh, Ahana and Tschiatschek, Sebastian and Mahdavi, Hamed and Singla, Adish},
  year = {2020},
  booktitle = {International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
  url = {http://ifaamas.org/Proceedings/aamas2020/pdfs/p447.pdf},
  tag = {SDM}
}

Beck, J., Ciosek, K., Devlin, S., Tschiatschek, S., Zhang, C., & Hofmann, K. (2020). AMRL: Aggregated Memory For Reinforcement Learning. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=Bkl7bREtDr

@inproceedings{Beck2020AMRL,
  title = {AMRL: Aggregated Memory For Reinforcement Learning},
  author = {Beck, Jacob and Ciosek, Kamil and Devlin, Sam and Tschiatschek, Sebastian and Zhang, Cheng and Hofmann, Katja},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Bkl7bREtDr},
  tag = {SDM}
}

Ma, C., Tschiatschek, S., Li, Y., Turner, R., Hernández-Lobato, J. M., & Zhang, C. (2020). HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. Proceedings of The 2nd Symposium On Advances in Approximate Bayesian Inference, 118, 1–8. http://proceedings.mlr.press/v118/ma20a/ma20a.pdf

@inproceedings{pmlr-v118-ma20a,
  title = { HM-VAEs: a Deep Generative Model for
   Real-valued Data with Heterogeneous Marginals},
  author = {Ma, Chao and Tschiatschek, Sebastian and Li, Yingzhen and Turner, Richard and Hernández-Lobato, José Miguel and Zhang, Cheng},
  booktitle = {Proceedings of The 2nd Symposium on
   Advances in Approximate Bayesian Inference},
  pages = {1--8},
  year = {2020},
  volume = {118},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  url = {http://proceedings.mlr.press/v118/ma20a/ma20a.pdf},
  tag = {PMI}
}

Roth, W., Schindler, G., Zöhrer, M., Pfeifenberger, L., Peharz, R., Tschiatschek, S., Fröning, H., Pernkopf, F., & Ghahramani, Z. (2020). Resource-Efficient Neural Networks for Embedded Systems. https://arxiv.org/pdf/2001.03048.pdf

@misc{roth2020resourceefficient,
  title = {Resource-Efficient Neural Networks for Embedded Systems},
  author = {Roth, Wolfgang and Schindler, Günther and Zöhrer, Matthias and Pfeifenberger, Lukas and Peharz, Robert and Tschiatschek, Sebastian and Fröning, Holger and Pernkopf, Franz and Ghahramani, Zoubin},
  year = {2020},
  eprint = {2001.03048},
  archiveprefix = {arXiv},
  primaryclass = {stat.ML},
  url = {https://arxiv.org/pdf/2001.03048.pdf}
}

2019

Ohrimenko, O., Tople, S., & Tschiatschek, S. (2019). Collaborative Machine Learning Markets with Data-Replication-Robust Payments. https://arxiv.org/pdf/1911.09052.pdf

@misc{ohrimenko2019collaborative,
  title = {Collaborative Machine Learning Markets with Data-Replication-Robust Payments},
  author = {Ohrimenko, Olga and Tople, Shruti and Tschiatschek, Sebastian},
  year = {2019},
  eprint = {1911.09052},
  archiveprefix = {arXiv},
  primaryclass = {cs.GT},
  url = {https://arxiv.org/pdf/1911.09052.pdf}
}

Gong, W., Tschiatschek, S., Turner, R., Nowozin, S., & Hernández-Lobato, J. M. (2019). Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. Neural Information Processing Systems (NeurIPS). https://papers.nips.cc/paper/9621-icebreaker-element-wise-efficient-information-acquisition-with-a-bayesian-deep-latent-gaussian-model.pdf

@inproceedings{gong2019icebreaker,
  title = {Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model},
  author = {Gong, Wenbo and Tschiatschek, Sebastian and Turner, Richard and Nowozin, Sebastian and Hernández-Lobato, José Miguel},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  url = {https://papers.nips.cc/paper/9621-icebreaker-element-wise-efficient-information-acquisition-with-a-bayesian-deep-latent-gaussian-model.pdf},
  year = {2019},
  tag = {PMI,SDM},
  teaserimage = {figures/pmi/gong2019icebreaker.png},
  teasercaption = {Accumulated feature statistics as active selection progresses.}
}

Tschiatschek, S., Ghosh, A., Haug, L., Devidze, R., & Singla, A. (2019). Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. Neural Information Processing Systems (NeurIPS). https://papers.nips.cc/paper/8668-learner-aware-teaching-inverse-reinforcement-learning-with-preferences-and-constraints.pdf

@inproceedings{tschiatschek2019learner,
  title = {Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints},
  author = {Tschiatschek, Sebastian and Ghosh, Ahana and Haug, Luis and Devidze, Rati and Singla, Adish},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  url = {https://papers.nips.cc/paper/8668-learner-aware-teaching-inverse-reinforcement-learning-with-preferences-and-constraints.pdf},
  year = {2019},
  tag = {SDM},
  teaserimage = {figures/sdm/neurips2019-learner-aware.png},
  teasercaption = {A gridworld environment and a learner's preferences}
}

Igl, M., Ciosek, K., Li, Y., Tschiatschek, S., Zhang, C., Devlin, S., & Hofmann, K. (2019). Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. Neural Information Processing Systems (NeurIPS). https://arxiv.org/pdf/1910.12911.pdf

@inproceedings{igl2019noiseinjection,
  title = {Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck},
  author = {Igl, Maximilian and Ciosek, Kamil and Li, Yingzhen and Tschiatschek, Sebastian and Zhang, Cheng and Devlin, Sam and Hofmann, Katja},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  tag = {SDM},
  url = {https://arxiv.org/pdf/1910.12911.pdf}
}

Janz, D., Hron, J., Mazur, P., Hofmann, K., Hernández-Lobato, J. M., & Tschiatschek, S. (2019). Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. Neural Information Processing Systems (NeurIPS). https://arxiv.org/pdf/1810.06530.pdf

@inproceedings{janz2019successor,
  title = {Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning},
  author = {Janz, David and Hron, Jiri and Mazur, Przemysław and Hofmann, Katja and Hernández-Lobato, José Miguel and Tschiatschek, Sebastian},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  url = {https://arxiv.org/pdf/1810.06530.pdf},
  year = {2019},
  tag = {SDM}
}

Ma, C., Gong, W., Tschiatschek, S., Nowozin, S., Hernández-Lobato, J. M., & Zhang, C. (2019). Bayesian EDDI: Sequential Variable Selection with Bayesian Partial VAE. Workshop on Real-World Sequential Decision Making: Reinforcement Learning and Beyond at NeurIPS. https://realworld-sdm.github.io/paper/26.pdf

@article{ma2019bayesian,
  title = {Bayesian EDDI: Sequential Variable Selection with Bayesian Partial VAE},
  author = {Ma, Chao and Gong, Wenbo and Tschiatschek, Sebastian and Nowozin, Sebastian and Hernández-Lobato, José Miguel and Zhang, Cheng},
  url = {https://realworld-sdm.github.io/paper/26.pdf},
  journal = {Workshop on Real-World Sequential Decision Making: Reinforcement Learning and Beyond at NeurIPS},
  year = {2019},
  tag = {PMI,SDM}
}

2018

Haug, L., Tschiatschek, S., & Singla, A. (2018). Teaching inverse reinforcement learners via features and demonstrations. Advances in Neural Information Processing Systems (NeurIPS). https://arxiv.org/abs/1810.08926

@inproceedings{haug2018teaching-risk,
  title = {Teaching inverse reinforcement learners via features and demonstrations},
  author = {Haug, Luis and Tschiatschek, Sebastian and Singla, Adish},
  year = {2018},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  teaserimage = {figures/sdm/neurips2018-teaching-risk.png},
  teasercaption = {Success of teaching for various teaching risks},
  tag = {SDM},
  url = {https://arxiv.org/abs/1810.08926}
}

Dresdner, G., Tschiatschek, S., Gál, V., & Rätsch, G. (2018). Optimally Searching for Cancer Genes Using Submodular Models. In Workshop on Computational Biology at International Conference on Machine Learning (ICML).

@techreport{dresdner2018optimally,
  title = {Optimally Searching for Cancer Genes Using Submodular Models},
  author = {Dresdner, Gideon and Tschiatschek, Sebastian and G{\'a}l, Viktor and R{\"a}tsch, Gunnar},
  year = {2018},
  booktitle = {Workshop on Computational Biology at International Conference on Machine Learning (ICML)},
  tag = {PMI}
}

Ratajczak, M., Tschiatschek, S., & Pernkopf, F. (2018). Sum-Product Networks for Sequence Labeling. https://arxiv.org/pdf/1807.02324.pdf

@misc{ratajczak2018sum,
  title = {Sum-Product Networks for Sequence Labeling},
  author = {Ratajczak, Martin and Tschiatschek, Sebastian and Pernkopf, Franz},
  year = {2018},
  eprint = {1807.02324},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/pdf/1807.02324.pdf},
  tag = {PMI}
}

Roth, W., Peharz, R., Tschiatschek, S., & Pernkopf, F. (2018). Hybrid Generative-Discriminative Training of Gaussian Mixture Models. Pattern Recognition Letters.

@article{roth2018hybrid,
  title = {Hybrid Generative-Discriminative Training of Gaussian Mixture Models},
  author = {Roth, Wolfgang and Peharz, Robert and Tschiatschek, Sebastian and Pernkopf, Franz},
  journal = {Pattern Recognition Letters},
  year = {2018},
  publisher = {Elsevier},
  tag = {PMI}
}

Tschiatschek, S., Arulkumaran, K., Stühmer, J., & Hofmann, K. (2018). Variational Inference for Data-Efficient Model Learning in POMDPs. https://arxiv.org/abs/1805.09281

@misc{tschiatschek2018variational,
  title = {Variational Inference for Data-Efficient Model Learning in POMDPs},
  author = {Tschiatschek, Sebastian and Arulkumaran, Kai and Stühmer, Jan and Hofmann, Katja},
  year = {2018},
  eprint = {1805.09281},
  archiveprefix = {arXiv},
  primaryclass = {stat.ML},
  url = {https://arxiv.org/abs/1805.09281},
  tag = {PMI,SDM}
}

Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018). Fake News Detection in Social Networks via Crowd Signals. Companion of the The Web Conference 2018 (WWW), 517–524. https://arxiv.org/pdf/1711.09025.pdf

@inproceedings{tschiatschek18fake,
  title = {Fake News Detection in Social Networks via Crowd Signals},
  author = {Tschiatschek, Sebastian and Singla, Adish and Gomez Rodriguez, Manuel and Merchant, Arpit and Krause, Andreas},
  booktitle = {Companion of the The Web Conference 2018 (WWW)},
  pages = {517--524},
  year = {2018},
  organization = {International World Wide Web Conferences Steering Committee},
  url = {https://arxiv.org/pdf/1711.09025.pdf},
  tag = {PMI}
}

Tschiatschek, S., Sahin, A., & Krause, A. (2018). Differentiable Submodular Maximization. Joint Conference on Artificial Intelligence (IJCAI), 2731–2738. https://www.ijcai.org/proceedings/2018/0379.pdf

@inproceedings{tschiatschek2018differentiable,
  title = {Differentiable Submodular Maximization},
  author = {Tschiatschek, Sebastian and Sahin, Aytunc and Krause, Andreas},
  booktitle = {Joint Conference on Artificial Intelligence (IJCAI)},
  pages = {2731--2738},
  year = {2018},
  month = jul,
  url = {https://www.ijcai.org/proceedings/2018/0379.pdf},
  long = {https://arxiv.org/abs/1803.01785},
  tag = {PMI}
}

Hirnschall, C., Singla, A., Tschiatschek, S., & Krause, A. (2018). Learning User Preferences to Incentivize Exploration in the Sharing Economy. Conference on Artificial Intelligence (AAAI). https://arxiv.org/pdf/1711.08331.pdf

@inproceedings{Hirnschall2018UserPreferences,
  author = {Hirnschall, Christhop and Singla, Adish and Tschiatschek, Sebastian and Krause, Andreas},
  title = {Learning User Preferences to Incentivize Exploration in the Sharing Economy},
  year = {2018},
  booktitle = {Conference on Artificial Intelligence (AAAI)},
  url = {https://arxiv.org/pdf/1711.08331.pdf}
}

2017

Wossnig, L., Tschiatschek, S., & Zohren, S. (2017). Quantum-classical truncated Newton method for high-dimensional energy landscapes. https://arxiv.org/pdf/1710.07063.pdf

@misc{Wossnig17Quantum,
  title = {Quantum-classical truncated Newton method for high-dimensional energy landscapes},
  author = {Wossnig, Leonard and Tschiatschek, Sebastian and Zohren, Stefan},
  year = {2017},
  eprint = {1710.07063},
  archiveprefix = {arXiv},
  primaryclass = {quant-ph},
  url = {https://arxiv.org/pdf/1710.07063.pdf}
}

Zhao, J., Djolonga, J., Tschiatschek, S., & Krause, A. (2017). Improving Optimization-Based Approximate Inference by Clamping Variables. Uncertainty in Artificial Intelligence (UAI). http://auai.org/uai2017/proceedings/papers/259.pdf

@inproceedings{Zhao17Clamping,
  author = {Zhao, Junyao and Djolonga, Josip and Tschiatschek, Sebastian and Krause, Andreas},
  title = {Improving Optimization-Based Approximate Inference by Clamping Variables},
  year = {2017},
  booktitle = {Uncertainty in Artificial Intelligence (UAI)},
  url = {http://auai.org/uai2017/proceedings/papers/259.pdf},
  long = {https://www.tschiatschek.net/files/zhao17clamping.pdf},
  tag = {PMI}
}

Ratajczak, M., Tschiatschek, S., & Pernkopf, F. (2017). Frame and Segment Level Recurrent Neural Networks for Phone Classification. Conference of the International Speech Communication Association (Interspeech), 1318–1322. https://www.tschiatschek.net/files/ratajczak17fsrnn.pdf

@inproceedings{Ratajczak17FSRNN,
  author = {Ratajczak, Martin and Tschiatschek, Sebastian and Pernkopf, Franz},
  booktitle = {Conference of the International Speech Communication Association (Interspeech)},
  year = {2017},
  title = {Frame and Segment Level Recurrent Neural Networks for Phone Classification},
  pages = {1318--1322},
  url = {https://www.tschiatschek.net/files/ratajczak17fsrnn.pdf},
  tag = {PMI}
}

Bian, A. A., Buhmann, J. M., Krause, A., & Tschiatschek, S. (2017). Guarantees for Greedy Maximization of Non-submodular Functions with Applications. International Conference on Machine Learning (ICML). https://arxiv.org/pdf/1703.02100.pdf

@inproceedings{Bian2017Approximate,
  author = {Bian, Andrew An and Buhmann, Joachim M. and Krause, Andreas and Tschiatschek, Sebastian},
  title = {Guarantees for Greedy Maximization of Non-submodular Functions with Applications},
  year = {2017},
  booktitle = {International Conference on Machine Learning (ICML)},
  url = {https://arxiv.org/pdf/1703.02100.pdf}
}

Tschiatschek, S., Singla, A., & Krause, A. (2017). Selecting Sequences of Items via Submodular Maximization. Conference on Artificial Intelligence (AAAI), 2667–2673. https://www.tschiatschek.net/files/tschiatschek17ordered.pdf

@inproceedings{Tschiatschek17Sequential,
  author = {Tschiatschek, Sebastian and Singla, Adish and Krause, Andreas},
  booktitle = {Conference on Artificial Intelligence (AAAI)},
  title = {Selecting Sequences of Items via Submodular Maximization},
  year = {2017},
  pages = {2667--2673},
  url = {https://www.tschiatschek.net/files/tschiatschek17ordered.pdf},
  long = {https://www.tschiatschek.net/files/tschiatschek17ordered-extended.pdf}
}

2016

Djolonga, J., Jegelka, S., Tschiatschek, S., & Krause, A. (2016). Cooperative Graphical Models. Neural Information Processing Systems (NIPS). https://papers.nips.cc/paper/6122-cooperative-graphical-models.pdf

@inproceedings{Djolonga16Cooperative,
  author = {Djolonga, Josip and Jegelka, Stefanie and Tschiatschek, Sebastian and Krause, Andreas},
  booktitle = {Neural Information Processing Systems (NIPS)},
  title = {Cooperative Graphical Models},
  year = {2016},
  url = {https://papers.nips.cc/paper/6122-cooperative-graphical-models.pdf},
  long = {https://www.tschiatschek.net/files/djolonga16cooperative.pdf},
  tag = {PMI}
}

Djolonga, J., Tschiatschek, S., & Krause, A. (2016). Variational Inference in Mixed Probabilistic Submodular Models. Neural Information Processing Systems (NIPS). https://papers.nips.cc/paper/6225-variational-inference-in-mixed-probabilistic-submodular-models.pdf

@inproceedings{Djolonga16Mixed,
  author = {Djolonga, Josip and Tschiatschek, Sebastian and Krause, Andreas},
  booktitle = {Neural Information Processing Systems (NIPS)},
  title = {Variational Inference in Mixed Probabilistic Submodular Models},
  year = {2016},
  url = {https://papers.nips.cc/paper/6225-variational-inference-in-mixed-probabilistic-submodular-models.pdf},
  tag = {PMI}
}

Ratajczak, M., Tschiatschek, S., & Pernkopf, F. (2016). Virtual Adversarial Training Applied to Neural Higher-Order Factors for Phone Classification. Conference of the International Speech Communication Association (Interspeech), 2756–2760. https://www.tschiatschek.net/files/ratajczak16vat.pdf

@inproceedings{Ratjczak16VAT,
  author = {Ratajczak, Martin and Tschiatschek, Sebastian and Pernkopf, Franz},
  booktitle = {Conference of the International Speech Communication Association (Interspeech)},
  year = {2016},
  title = {Virtual Adversarial Training Applied to Neural Higher-Order Factors for Phone Classification},
  url = {https://www.tschiatschek.net/files/ratajczak16vat.pdf},
  pages = {2756--2760},
  tag = {PMI}
}

Singla, A., Tschiatschek, S., & Krause, A. (2016, June). Actively Learning Hemimetrics with Applications to Eliciting User Preferences. International Conference on Machine Learning (ICML). https://www.tschiatschek.net/files/singla16hemimetric.pdf

@inproceedings{Singla16Hemimetric,
  author = {Singla, Adish and Tschiatschek, Sebastian and Krause, Andreas},
  booktitle = {International Conference on Machine Learning (ICML)},
  title = {Actively Learning Hemimetrics with Applications to Eliciting User Preferences},
  year = {2016},
  month = jun,
  url = {https://www.tschiatschek.net/files/singla16hemimetric.pdf},
  long = {https://www.tschiatschek.net/files/singla16hemimetric_long.pdf}
}

Tschiatschek, S., Djolonga, J., & Krause, A. (2016, May). Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation. International Conference on Artificial Intelligence and Statistics (AISTATS). https://www.tschiatschek.net/files/tschiatschek16learning.pdf

@inproceedings{Tschiatschek16diversity,
  author = {Tschiatschek, Sebastian and Djolonga, Josip and Krause, Andreas},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  month = may,
  title = {Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation},
  year = {2016},
  url = {https://www.tschiatschek.net/files/tschiatschek16learning.pdf},
  long = {https://www.tschiatschek.net/files/tschiatschek16learning_long.pdf},
  tag = {PMI}
}

Singla, A., Tschiatschek, S., & Krause, A. (2016). Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization. Conference on Artificial Intelligence (AAAI). https://www.tschiatschek.net/files/singla16noisy.pdf

@inproceedings{Singla2016Noisy,
  author = {Singla, Adish and Tschiatschek, Sebastian and Krause, Andreas},
  title = {{Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization}},
  booktitle = {Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://www.tschiatschek.net/files/singla16noisy.pdf},
  long = {https://www.tschiatschek.net/files/singla16noisy_long.pdf}
}

2015

Ratajczak, M., Tschiatschek, S., & Pernkopf, F. (2015). Neural Higher-Order Factors in Conditional Random Fields for Phoneme Classification. Conference of the International Speech Communication Association (INTERSPEECH). https://www.tschiatschek.net/files/ratajczak15neural-higher-order.pdf

@inproceedings{Ratajczak2015interspeech,
  author = {Ratajczak, Martin and Tschiatschek, Sebastian and Pernkopf, Franz},
  title = {{Neural Higher-Order Factors in Conditional Random Fields for Phoneme Classification}},
  booktitle = {Conference of the International Speech Communication Association (INTERSPEECH)},
  url = {https://www.tschiatschek.net/files/ratajczak15neural-higher-order.pdf},
  year = {2015}
}

Peharz, R., Tschiatschek, S., Pernkopf, F., & Domingos, P. M. (2015). On Theoretical Properties of Sum-Product Networks. International Conference on Artificial Intelligence and Statistics (AISTATS). https://www.tschiatschek.net/files/peharz15theoretical.pdf

@inproceedings{Peharz2015Theoretical,
  author = {Peharz, Robert and Tschiatschek, Sebastian and Pernkopf, Franz and Domingos, Pedro M.},
  title = {{On Theoretical Properties of Sum-Product Networks}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = {2015},
  url = {https://www.tschiatschek.net/files/peharz15theoretical.pdf},
  long = {https://www.tschiatschek.net/files/peharz15theoretical-supp.pdf},
  tag = {PMI}
}

Tschiatschek, S., & Pernkopf, F. (2015). Parameter Learning of Bayesian Network Classifiers Under Computational Constraints. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 86–101.

@inproceedings{TschiatschekP15,
  author = {Tschiatschek, Sebastian and Pernkopf, Franz},
  title = {{Parameter Learning of Bayesian Network Classifiers Under Computational
                 Constraints}},
  booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},
  pages = {86--101},
  year = {2015}
}

Ratajczak, M., Tschiatschek, S., & Pernkopf, F. (2015). Structured Regularizer for Neural Higher-Order Sequence Models. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 168–183. https://www.tschiatschek.net/files/ratajczak15StructuredRegularizer.pdf

@inproceedings{RatajczakTP15,
  author = {Ratajczak, Martin and Tschiatschek, Sebastian and Pernkopf, Franz},
  title = {{Structured Regularizer for Neural Higher-Order Sequence Models}},
  booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},
  pages = {168--183},
  year = {2015},
  url = {https://www.tschiatschek.net/files/ratajczak15StructuredRegularizer.pdf},
  tag = {PMI}
}

Knoll, C., Rath, M., Tschiatschek, S., & Pernkopf, F. (2015). Message Scheduling Methods for Belief Propagation. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 295–310. https://www.tschiatschek.net/files/knoll15MessageScheduling.pdf

@inproceedings{Knoll2015MessageScheduling,
  author = {Knoll, Christian and Rath, Michael and Tschiatschek, Sebastian and Pernkopf, Franz},
  title = {{Message Scheduling Methods for Belief Propagation}},
  booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},
  pages = {295--310},
  year = {2015},
  url = {https://www.tschiatschek.net/files/knoll15MessageScheduling.pdf},
  tag = {PMI}
}

Tschiatschek, S., & Pernkopf, F. (2015). On Reduced Precision Bayesian Network Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 37(4). https://www.tschiatschek.net/files/tschiatschek15OnReducedPrecisionBNCs.pdf

@article{Tschiatschek2014TPAMI,
  author = {Tschiatschek, Sebastian and Pernkopf, Franz},
  title = {{On Reduced Precision Bayesian Network Classifiers.}},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year = {2015},
  volume = {37},
  number = {4},
  url = {https://www.tschiatschek.net/files/tschiatschek15OnReducedPrecisionBNCs.pdf}
}

2014

Tschiatschek, S., Paul, K., & Pernkopf, F. (2014). Integer Bayesian Network Classifiers. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 209–224.

@inproceedings{Tschiatschek2014ReducedPrecisionParameterLearning,
  author = {Tschiatschek, Sebastian and Paul, Karin and Pernkopf, Franz},
  title = {{Integer Bayesian Network Classifiers}},
  booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},
  pages = {209--224},
  year = {2014}
}

Tschiatschek, S., Iyer, R., Wei, H., & Bilmes, J. (2014). Learning Mixtures of Submodular Functions for Image Collection Summarization. Neural Information Processing Systems (NIPS). https://www.tschiatschek.net/files/tschiatschek14summarization.pdf

@inproceedings{Tschiatschek2014ImageSummarizaiton,
  author = {Tschiatschek, Sebastian and Iyer, Rishabh and Wei, Haochen and Bilmes, Jeff},
  booktitle = {Neural Information Processing Systems (NIPS)},
  title = {{Learning Mixtures of Submodular Functions for
  Image Collection Summarization}},
  year = {2014},
  url = {https://www.tschiatschek.net/files/tschiatschek14summarization.pdf}
}

Ratajczak, M., Tschiatschek, S., & Pernkopf, F. (2014). Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields. https://www.tschiatschek.net/files/ratajczak14spns.pdf

@techreport{Ratajczak2014,
  author = {Ratajczak, Martin and Tschiatschek, Sebastian and Pernkopf, Franz},
  year = {2014},
  title = {{Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields}},
  url = {https://www.tschiatschek.net/files/ratajczak14spns.pdf},
  tag = {PMI}
}

Pernkopf, F., Peharz, R., & Tschiatschek, S. (2014). Introduction to Probabilistic Graphical Models. In Academic Press Library in Signal Processing (Vol. 1, pp. 989–1064). Elsevier.

@inbook{Pernkopf2014Introduction,
  title = {{Introduction to Probabilistic Graphical Models}},
  booktitle = {Academic Press Library in Signal Processing},
  volume = {1},
  chapter = {18},
  year = {2014},
  pages = {989-1064},
  author = {Pernkopf, Franz and Peharz, Robert and Tschiatschek, Sebastian},
  publisher = {Elsevier},
  tag = {PMI}
}

2013

Peharz, R., Tschiatschek, S., & Pernkopf, F. (2013). The Most Generative Maximum Margin Bayesian Networks. International Conference on Machine Learning (ICML), 28, 235–243. https://www.tschiatschek.net/files/peharz13MostGenerativeMMBN.pdf

@inproceedings{Peharz2013MostGenerative,
  author = {Peharz, Robert and Tschiatschek, Sebastian and Pernkopf, Franz},
  booktitle = {International Conference on Machine Learning (ICML)},
  title = {{The Most Generative Maximum Margin Bayesian Networks}},
  volume = {28},
  year = {2013},
  pages = {235--243},
  url = {https://www.tschiatschek.net/files/peharz13MostGenerativeMMBN.pdf},
  long = {https://www.tschiatschek.net/files/peharz13MostGenerativeMMBN-supp.pdf},
  tag = {PMI}
}

Tschiatschek, S., Cancino Chacón, C. E., & Pernkopf, F. (2013). Bounds for Bayesian Network Classifiers with Reduced Precision Parameters. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3357–3361. https://www.tschiatschek.net/files/tschiatschek13Bounds.pdf

@inproceedings{Tschiatschek2013BoundsReducedPrecision,
  author = {Tschiatschek, Sebastian and Cancino Chac\'o{}n, Carlos Eduardo and Pernkopf, Franz},
  booktitle = {International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title = {{Bounds for Bayesian Network Classifiers with Reduced Precision Parameters}},
  year = {2013},
  pages = {3357--3361},
  url = {https://www.tschiatschek.net/files/tschiatschek13Bounds.pdf}
}

Tschiatschek, S., & Pernkopf, F. (2013). Asymptotic Optimality of Maximum Margin Bayesian Networks. International Conference on Artificial Intelligence and Statistics (AISTATS), 590–598.

@inproceedings{Tschiatschek2013Asymptotic,
  title = {{Asymptotic Optimality of Maximum Margin Bayesian Networks}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year = {2013},
  pages = {590-598},
  author = {Tschiatschek, Sebastian and Pernkopf, Franz},
  tag = {PMI}
}

2012

Tschiatschek, S., Mutsam, N., & Pernkopf, F. (2012). Handling Missing Features in Maximum Margin Bayesian Network Classifiers. International Workshop on Machine Learning for Signal Processing (MLSP), 1–6.

@inproceedings{Tschiatschek2012MissingFeatures,
  author = {Tschiatschek, Sebastian and Mutsam, Nikolaus and Pernkopf, Franz},
  booktitle = {International Workshop on Machine Learning for Signal Processing (MLSP)},
  title = {{Handling Missing Features in Maximum Margin Bayesian Network Classifiers}},
  year = {2012},
  pages = {1-6},
  tag = {PMI}
}

Tschiatschek, S., Reinprecht, P., Mücke, M., & Pernkopf, F. (2012). Bayesian Network Classifiers with Reduced Precision Parameters. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 74–89.

@inproceedings{Tschiatschek2012ReducedPrecision,
  author = {Tschiatschek, Sebastian and Reinprecht, Peter and M\"{u}cke, Manfred and Pernkopf, Franz},
  title = {{Bayesian Network Classifiers with Reduced Precision Parameters}},
  booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},
  year = {2012},
  location = {Bristol, UK},
  pages = {74--89},
  numpages = {16}
}

Tschiatschek, S., & Pernkopf, F. (2012). Convex Combinations of Maximum Margin Bayesian Network Classifiers. International Conference on Pattern Recognition Applications and Methods (ICPRAM).

@inproceedings{Tschiatschek2012Convex,
  title = {{Convex Combinations of Maximum Margin Bayesian Network Classifiers}},
  booktitle = {International Conference on Pattern Recognition Applications and Methods (ICPRAM)},
  year = {2012},
  author = {Tschiatschek, Sebastian and Pernkopf, Franz}
}

Pernkopf, F., Wohlmayr, M., & Tschiatschek, S. (2012). Maximum Margin Bayesian Network Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 34(3), 521–531. https://www.tschiatschek.net/files/pernkopf12mmbn.pdf

@article{Pernkopf2012MMBN,
  author = {Pernkopf, Franz and Wohlmayr, Michael and Tschiatschek, Sebastian},
  title = {{Maximum Margin Bayesian Network Classifiers}},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year = {2012},
  volume = {34},
  number = {3},
  pages = {521--531},
  url = {https://www.tschiatschek.net/files/pernkopf12mmbn.pdf}
}