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ORCIDMartin Strobel 0001
Person information
- affiliation: National University of Singapore, Singapore
Other persons with the same name
- Martin Strobel 0002 — Maastricht University, Maastricht, The Netherlands
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2020 – today
- 2024
[j2]Xudong Shen, Hannah Brown, Jiashu Tao
, Martin Strobel, Yao Tong, Akshay Narayan, Harold Soh, Finale Doshi-Velez:
Directions of Technical Innovation for Regulatable AI Systems. Commun. ACM 67(11): 82-89 (2024)- 2023
- [c11]Prakhar Ganesh, Hongyan Chang, Martin Strobel, Reza Shokri:
On The Impact of Machine Learning Randomness on Group Fairness. FAccT 2023: 1789-1800 - [i7]Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Martin Strobel, Balaji Lakshminarayanan, Deepak Ramachandran:
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. CoRR abs/2302.05807 (2023) - [i6]Xudong Shen, Hannah Brown, Jiashu Tao, Martin Strobel, Yao Tong, Akshay Narayan, Harold Soh, Finale Doshi-Velez:
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities. CoRR abs/2306.12609 (2023) - [i5]Prakhar Ganesh, Hongyan Chang, Martin Strobel, Reza Shokri:
On The Impact of Machine Learning Randomness on Group Fairness. CoRR abs/2307.04138 (2023) - 2022
- [j1]Martin Strobel, Reza Shokri:
Data Privacy and Trustworthy Machine Learning. IEEE Secur. Priv. 20(5): 44-49 (2022) - [i4]Martin Strobel, Reza Shokri:
Data Privacy and Trustworthy Machine Learning. CoRR abs/2209.06529 (2022) - 2021
- [c10]Reza Shokri, Martin Strobel, Yair Zick:
On the Privacy Risks of Model Explanations. AIES 2021: 231-241 - [c9]Neel Patel, Martin Strobel, Yair Zick:
High Dimensional Model Explanations: An Axiomatic Approach. FAccT 2021: 401-411 - 2020
- [i3]Neel Patel, Martin Strobel, Yair Zick:
High Dimensional Model Explanations: an Axiomatic Approach. CoRR abs/2006.08969 (2020)
2010 – 2019
- 2019
- [c8]Jakub Sliwinski, Martin Strobel, Yair Zick:
Axiomatic Characterization of Data-Driven Influence Measures for Classification. AAAI 2019: 718-725 - [c7]Felix Brandt, Johannes Hofbauer, Martin Strobel:
Exploring the No-Show Paradox for Condorcet Extensions Using Ehrhart Theory and Computer Simulations. AAMAS 2019: 520-528 - [c6]Martin Strobel:
Aspects of Transparency in Machine Learning. AAMAS 2019: 2449-2451 - [i2]Reza Shokri, Martin Strobel, Yair Zick:
Privacy Risks of Explaining Machine Learning Models. CoRR abs/1907.00164 (2019) - 2018
- [c5]Xinrun Wang, Bo An, Martin Strobel, Fookwai Kong:
Catching Captain Jack: Efficient Time and Space Dependent Patrols to Combat Oil-Siphoning in International Waters. AAAI 2018: 208-215 - [c4]Martin Strobel:
An Axiomatic Approach to Explain Computer Generated Decisions: Extended Abstract. AIES 2018: 380-381 - [c3]Jakub Sliwinski, Martin Strobel, Yair Zick:
An Axiomatic Approach to Linear Explanations in Data Classification. IUI Workshops 2018 - 2017
- [i1]Jakub Sliwinski, Martin Strobel, Yair Zick:
A Characterization of Monotone Influence Measures for Data Classification. CoRR abs/1708.02153 (2017) - 2016
- [c2]Felix Brandt, Christian Geist, Martin Strobel:
Analyzing the Practical Relevance of Voting Paradoxes via Ehrhart Theory, Computer Simulations, and Empirical Data. AAMAS 2016: 385-393 - 2015
- [c1]Florian Brandl, Felix Brandt, Martin Strobel:
Fractional Hedonic Games: Individual and Group Stability. AAMAS 2015: 1219-1227
Coauthor Index
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