Follow
Martin Strobel
Martin Strobel
Verified email at comp.nus.edu.sg
Title
Cited by
Cited by
Year
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
1092021
Fractional hedonic games: Individual and group stability
F Brandl, F Brandt, M Strobel
Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015
542015
On the Privacy Risks of Model Explanations
R Shokri, M Strobel, Y Zick
arXiv preprint arXiv:1907.00164, 2019
39*2019
Data Privacy and Trustworthy Machine Learning
M Strobel, R Shokri
IEEE Security & Privacy, 2-7, 2022
252022
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
242019
Aspects of Transparency in Machine Learning
M Strobel
Proceedings of the 18th International Conference on Autonomous Agents and …, 2019
242019
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
242016
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
222019
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
162023
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
162018
High Dimensional Model Explanations: an Axiomatic Approach
N Patel, M Strobel, Y Zick
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021
122021
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
112020
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
102017
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
82021
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
72021
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
32023
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
32018
An Axiomatic Approach to Linear Explanations in Data Classification.
J Sliwinski, M Strobel, Y Zick
IUI Workshops, 2018
22018
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
2*
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
12023
The system can't perform the operation now. Try again later.
Articles 1–20