1.6k citations · 1.7k across the 20 of their papers we have counts for
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Cybercasing 2.0: You Get What You Pay For
Jaeyoung Choi, Istemi Ekin Akkus, Serge Egelman +4
Under U.S. law, marketing databases exist under almost no legal restrictions concerning accuracy, access, or confidentiality. We explore the possible (mis)use of these databases in…
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
Jingkang Wang, Ruoxi Jia, Gerald Friedland +2
Despite the great success achieved in machine learning (ML), adversarial examples have caused concerns with regards to its trustworthiness: A small perturbation of an input results…
A Practical Approach to Sizing Neural Networks
Gerald Friedland, Alfredo Metere, Mario Krell
Memorization is worst-case generalization. Based on MacKay's information theoretic model of supervised machine learning, this article discusses how to practically estimate the maxi…
The Accuracy of the Demographic Inferences Shown on Google's Ad Settings
Michael Carl Tschantz, Serge Egelman, Jaeyoung Choi +2
Google's Ad Settings shows the gender and age that Google has inferred about a web user. We compare the inferred values to the self-reported values of 501 survey participants. We f…
The Helmholtz Method: Using Perceptual Compression to Reduce Machine Learning Complexity
Gerald Friedland, Jingkang Wang, Ruoxi Jia +1
This paper proposes a fundamental answer to a frequently asked question in multimedia computing and machine learning: Do artifacts from perceptual compression contribute to error i…
Principle of Conservation of Computational Complexity
Gerald Friedland, Alfredo Metere
In this manuscript, we derive the principle of conservation of computational complexity. We measure computational complexity as the number of binary computations (decisions) requir…