| 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 | 124 | 2021 |
| Fractional hedonic games: Individual and group stability F Brandl, F Brandt, M Strobel Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015 | 54 | 2015 |
| On the Privacy Risks of Model Explanations R Shokri, M Strobel, Y Zick arXiv preprint arXiv:1907.00164, 2019 | 43* | 2019 |
| Data Privacy and Trustworthy Machine Learning M Strobel, R Shokri IEEE Security & Privacy, 2-7, 2022 | 33 | 2022 |
| 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 | 26 | 2019 |
| Aspects of Transparency in Machine Learning M Strobel Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 25 | 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 | 24 | 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 | 23 | 2019 |
| On The Impact of Machine Learning Randomness on Group Fairness P Ganesh, H Chang, M Strobel, R Shokri Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 22 | 2023 |
| 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 | 16 | 2018 |
| High Dimensional Model Explanations: an Axiomatic Approach N Patel, M Strobel, Y Zick Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 12 | 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 | 12 | 2020 |
| A characterization of monotone influence measures for data classification J Sliwinski, M Strobel, Y Zick Proceedings of the Workshop on Explainable AI (XAI); International Joint …, 2017 | 11 | 2017 |
| Exploring the no-show paradox for Condorcet extensions F Brandt, J Hofbauer, M Strobel Evaluating Voting Systems with Probability Models: Essays by and in Honor of …, 2021 | 8 | 2021 |
| 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: Essays by and in Honor of …, 2021 | 7 | 2021 |
| Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play JZ Liu, KD Dvijotham, J Lee, Q Yuan, M Strobel, B Lakshminarayanan, ... arXiv preprint arXiv:2302.05807, 2023 | 3 | 2023 |
| 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 | 3 | 2018 |
| Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities X Shen, H Brown, J Tao, M Strobel, Y Tong, A Narayan, H Soh, ... arXiv preprint arXiv:2306.12609, 2023 | 2 | 2023 |
| An Axiomatic Approach to Linear Explanations in Data Classification. J Sliwinski, M Strobel, Y Zick IUI Workshops, 2018 | 2 | 2018 |
| Pushing the Accuracy-Fairness Tradeoff Frontier with Introspective Self-play JZ Liu, KD Dvijotham, J Lee, Q Yuan, M Strobel, B Lakshminarayanan, ... NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and …, 0 | 1* | |
