Fractional hedonic games: Individual and group stability F Brandl, F Brandt, M Strobel Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015 | 40 | 2015 |

On the Privacy Risks of Model Explanations R Shokri, M Strobel, Y Zick arXiv preprint arXiv:1907.00164, 2019 | 27* | 2019 |

Analyzing the practical relevance of voting paradoxes via Ehrhart theory, computer simulations, and empirical data F Brandt, C Geist, M Strobel Proceedings of the 2016 International Conference on Autonomous Agents …, 2016 | 19 | 2016 |

Exploring the no-show paradox for Condorcet extensions using Ehrhart theory and computer simulations F Brandt, J Hofbauer, M Strobel Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 16 | 2019 |

On the privacy risks of model explanations R Shokri, M Strobel, Y Zick Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 231-241, 2021 | 11 | 2021 |

Catching Captain Jack: Efficient time and space dependent patrols to combat oil-siphoning in international waters X Wang, B An, M Strobel, F Kong Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 11 | 2018 |

Axiomatic characterization of data-driven influence measures for classification J Sliwinski, M Strobel, Y Zick Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 718-725, 2019 | 10 | 2019 |

A characterization of monotone influence measures for data classification J Sliwinski, M Strobel, Y Zick CoRR abs/1708.02153, 2018 | 8 | 2018 |

Aspects of Transparency in Machine Learning M Strobel Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 5 | 2019 |

Analyzing the practical relevance of the Condorcet loser paradox and the agenda contraction paradox F Brandt, C Geist, M Strobel Evaluating Voting Systems with Probability Models, 97-115, 2021 | 4 | 2021 |

Exploiting Transparency Measures for Membership Inference: a Cautionary Tale R Shokri, M Strobel, Y Zick The AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI). AAAI 13, 2020 | 4 | 2020 |

High Dimensional Model Explanations: an Axiomatic Approach N Patel, M Strobel, Y Zick Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 2 | 2021 |

Exploring the no-show paradox for Condorcet extensions F Brandt, J Hofbauer, M Strobel Evaluating Voting Systems with Probability Models, 251-273, 2021 | 2 | 2021 |

An Axiomatic Approach to Explain Computer Generated Decisions M Strobel Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 380-381, 2018 | 2 | 2018 |

An Axiomatic Approach to Linear Explanations in Data Classification. J Sliwinski, M Strobel, Y Zick IUI Workshops, 2018 | 1 | 2018 |

Poster: Privacy Risks of Explaining Machine Learning Models R Shokri, M Strobel, Y Zick | | |