Publications

Found 25 results
Author [ Title(Asc)] Type Year
Filters: Author is Michael Tschantz  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
R
Miller, B., Kantchelian A., Tschantz M. Carl, Afroz S., Bachwani R., Faizullabhoy R., et al. (2016).  Reviewer Integration and Performance Measurement for Malware Detection. 13th Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA).
P
Tschantz, M. Carl, & Friedland G. (2019).  Privacy Concerns of Multimodal Sensor Systems. The Handbook of Multimodal-Multisensor Interfaces: Foundations, User Modeling, and Common Modality Combinations . 1,
M
Tschantz, M. Carl, Afroz S., Paxson V., & Tygar J.D.. (2014).  On Modeling the Costs of Censorship. CoRR. abs/1409.3211,
Tschantz, M. Carl, Datta A., Datta A., & Wing J. M. (2015).  A Methodology for Information Flow Experiments. 2015 IEEE 28th Computer Security Foundations Symposium. 554-568.
L
Kantchelian, A., Tschantz M. Carl, Huang L., Bartlett P. L., Joseph A. D., & Tygar J.D.. (2014).  Large-margin Convex Polytope Machine. Proceedings of the 27th International Conference on Neural Information Processing Systems. 3248–3256.
H
Lee, M. Kyung, Grgić-Hlača N., Tschantz M. Carl, Binns R., Weller A., Carney M., et al. (2020).  Human-Centered Approaches to Fair and Responsible AI. CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1-8.
Saha, D., Schumann C., McElfresh D. C., Dickerson J. P., Mazurek M. L., & Tschantz M. Carl (2020).  Human Comprehension of Fairness in Machine Learning. AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 152.
A
Yeom, S., & Tschantz M. (2021).  Avoiding Disparity Amplification under Different Worldviews. FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 273-283.
Datta, A., Tschantz M. Carl, & Datta A. (2015).  Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination. Proceedings on Privacy Enhancing Technologies. 2015(1), 92-112.
Plane, A. C., Redmiles E. M., Mazurek M. L., & Tschantz M. Carl (2017).  Assessing user perceptions of online targeted advertising. Proceedings of USENIX Security 2017.
Arefi, M. Navaki, Pandi R., Tschantz M. Carl, Crandall J. R., Fu K-wa., Shi D. Qiu, et al. (2019).  Assessing Post Deletion in Sina Weibo: Multi-modal Classification of Hot Topics.
Miller, B., Kantchelian A., Afroz S., Bachwani R., Dauber E., Huang L., et al. (2014).  Adversarial Active Learning. Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (AISec '14). 3–14.
Tschantz, M. Carl, Egelman S., Choi J., Weaver N., & Friedland G. (2016).  The Accuracy of the Demographic Inferences Shown on Google's Ad Settings.
Tschantz, M. Carl, Egelman S., Choi J., Weaver N., & Friedland G. (2018).  The Accuracy of the Demographic Inferences Shown on Google's Ad Settings. Proceedings of the 2018 Workshop on Privacy in the Electronic Society. 33-41.