1 code implementation • 28 Nov 2022 • Anindya Sarkar, Michael Lanier, Scott Alfeld, Jiarui Feng, Roman Garnett, Nathan Jacobs, Yevgeniy Vorobeychik
Many problems can be viewed as forms of geospatial search aided by aerial imagery, with examples ranging from detecting poaching activity to human trafficking.
no code implementations • 15 Aug 2022 • Ara Vartanian, Will Rosenbaum, Scott Alfeld
We distill this goal to the task of performing a training-set data insertion attack against $k$-Nearest Neighbor classification ($k$NN).
no code implementations • 29 Jun 2022 • Zhifeng Kong, Scott Alfeld
Using this framework, we introduce a fast method for approximate data deletion and a statistical test for estimating whether or not training points have been deleted.
1 code implementation • 17 Sep 2021 • Neil G. Marchant, Benjamin I. P. Rubinstein, Scott Alfeld
The right to erasure requires removal of a user's information from data held by organizations, with rigorous interpretations extending to downstream products such as learned models.
no code implementations • 16 Mar 2021 • David Liu, Zohair Shafi, William Fleisher, Tina Eliassi-Rad, Scott Alfeld
We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO).
1 code implementation • 12 Aug 2020 • Sixie Yu, Leonardo Torres, Scott Alfeld, Tina Eliassi-Rad, Yevgeniy Vorobeychik
However, in many applications, such as targeted vulnerability assessment or clinical therapies, one aspires to affect a targeted subset of a network, while limiting the impact on the rest.
Social and Information Networks Physics and Society
1 code implementation • ICML 2018 • Liang Tong, Sixie Yu, Scott Alfeld, Yevgeniy Vorobeychik
We present an algorithm for computing this equilibrium, and show through extensive experiments that equilibrium models are significantly more robust than conventional regularized linear regression.
no code implementations • 13 Jun 2015 • Matthew L. Malloy, Scott Alfeld, Paul Barford
Our approach considers the normal condition of the data to be specified by a model consisting of a set of distributions.