• Publications
  • Influence
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certainExpand
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Online Learning of Multiple Tasks and Their Relationships
We propose an Online MultiTask Learning (Omtl) framework which simultaneously learns the task weight vectors as well as the task relatedness adaptively from the data. Our work is in contrast withExpand
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The geometric median on Riemannian manifolds with application to robust atlas estimation
One of the primary goals of computational anatomy is the statistical analysis of anatomical variability in large populations of images. The study of anatomical shape is inherently related to theExpand
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On external memory graph traversal
We describe a new external memory data structure, the buffered repository tree, and use it to provide the first non-trivial external memory algorithm for directed breadth-first search (BFS) and anExpand
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Robust statistics on Riemannian manifolds via the geometric median
The geometric median is a classic robust estimator of centrality for data in Euclidean spaces. In this paper we formulate the geometric median of data on a Riemannian manifold as the minimizer of theExpand
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Fairness and Abstraction in Sociotechnical Systems
A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and dueExpand
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The Connectivity Server: Fast Access to Linkage Information on the Web
Abstract We have built a server that provides linkage information for all pages indexed by the AltaVista search engine. In its basic operation, the server accepts a query consisting of a set L of oneExpand
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On minimizing budget and time in influence propagation over social networks
In recent years, study of influence propagation in social networks has gained tremendous attention. In this context, we can identify three orthogonal dimensions—the number of seed nodes activated atExpand
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On the (im)possibility of fairness
What does it mean for an algorithm to be fair? Different papers use different notions of algorithmic fairness, and although these appear internally consistent, they also seem mutually incompatible.Expand
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A comparative study of fairness-enhancing interventions in machine learning
Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result,Expand
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