Martin Strobel
Martin Strobel
Verified email at comp.nus.edu.sg
Title
Cited by
Cited by
Year
Fractional hedonic games: Individual and group stability
F Brandl, F Brandt, M Strobel
Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015
382015
On the Privacy Risks of Model Explanations
R Shokri, M Strobel, Y Zick
arXiv preprint arXiv:1907.00164, 2019
232019
Exploring the no-show paradox for Condorcet extensions
F Brandt, J Hofbauer, M Strobel
Evaluating Voting Systems with Probability Models, 251-273, 2021
172021
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
17*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
172016
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
112018
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
82019
Aspects of Transparency in Machine Learning
M Strobel
Proceedings of the 18th International Conference on Autonomous Agents and …, 2019
62019
A characterization of monotone influence measures for data classification
J Sliwinski, M Strobel, Y Zick
CoRR abs/1708.02153, 2018
62018
On the Privacy Risks of Model Explanations
R Shokri, M Strobel, Y Zick
arXiv preprint arXiv:1907.00164, 2019
52019
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
42021
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
32020
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
22018
An Axiomatic Approach to Linear Explanations in Data Classification.
J Sliwinski, M Strobel, Y Zick
IUI Workshops, 2018
12018
High Dimensional Model Explanations: an Axiomatic Approach
N Patel, M Strobel, Y Zick
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021
2021
Poster: Privacy Risks of Explaining Machine Learning Models
R Shokri, M Strobel, Y Zick
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Articles 1–16