Martin Strobel

According to our database, Martin Strobel authored at least 17 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Directions of Technical Innovation for Regulatable AI Systems.

Xudong Shen, Hannah Brown, Jiashu Tao, Martin Strobel, Yao Tong, Akshay Narayan, Harold Soh, Finale Doshi-Velez

Commun. ACM, November, 2024

2023

Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities.

Xudong Shen, Hannah Brown, Jiashu Tao, Martin Strobel, Yao Tong, Akshay Narayan, Harold Soh, Finale Doshi-Velez

CoRR, 2023

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play.

Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Martin Strobel, Balaji Lakshminarayanan, Deepak Ramachandran

CoRR, 2023

On The Impact of Machine Learning Randomness on Group Fairness.

Prakhar Ganesh, Hongyan Chang, Martin Strobel, Reza Shokri

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

2022

Data Privacy and Trustworthy Machine Learning.

Martin Strobel, Reza Shokri

IEEE Secur. Priv., 2022

2021

High Dimensional Model Explanations: An Axiomatic Approach.

Neel Patel, Martin Strobel, Yair Zick

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

On the Privacy Risks of Model Explanations.

Reza Shokri, Martin Strobel, Yair Zick

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2019

Privacy Risks of Explaining Machine Learning Models.

Reza Shokri, Martin Strobel, Yair Zick

CoRR, 2019

Aspects of Transparency in Machine Learning.

Martin Strobel

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

Exploring the No-Show Paradox for Condorcet Extensions Using Ehrhart Theory and Computer Simulations.

Felix Brandt, Johannes Hofbauer, Martin Strobel

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

Axiomatic Characterization of Data-Driven Influence Measures for Classification.

Jakub Sliwinski, Martin Strobel, Yair Zick

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

An Axiomatic Approach to Linear Explanations in Data Classification.

Jakub Sliwinski, Martin Strobel, Yair Zick

Proceedings of the Joint Proceedings of the ACM IUI 2018 Workshops co-located with the 23rd ACM Conference on Intelligent User Interfaces (ACM IUI 2018), 2018

An Axiomatic Approach to Explain Computer Generated Decisions: Extended Abstract.

Martin Strobel

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

Catching Captain Jack: Efficient Time and Space Dependent Patrols to Combat Oil-Siphoning in International Waters.

Xinrun Wang, Bo An, Martin Strobel, Fookwai Kong

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

A Characterization of Monotone Influence Measures for Data Classification.

Jakub Sliwinski, Martin Strobel, Yair Zick

CoRR, 2017

2016

Analyzing the Practical Relevance of Voting Paradoxes via Ehrhart Theory, Computer Simulations, and Empirical Data.

Felix Brandt, Christian Geist, Martin Strobel

Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

2015

Fractional Hedonic Games: Individual and Group Stability.

Florian Brandl, Felix Brandt, Martin Strobel

Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 2015