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Randomized gossip algorithms
TLDR
This work analyzes the averaging problem under the gossip constraint for an arbitrary network graph, and finds that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly stochastic matrix characterizing the algorithm. Expand
Gossip algorithms: design, analysis and applications
TLDR
This work analyzes the averaging problem under the gossip constraint for arbitrary network, and finds that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly stochastic matrix characterizing the algorithm. Expand
Universally utility-maximizing privacy mechanisms
TLDR
Every potential user u, no matter what its side information and preferences, derives as much utility from M* as from interacting with a differentially private mechanism Mu that is optimally tailored to u, subject to differential privacy. Expand
Input-Output Approach in an Allocation System
An input-output transaction matrix may be conceived in terms of an equilibrium position of two sets of interacting forces. The broadest way in which we can define them is 'to denote one set of forcesExpand
Crowdsourced judgement elicitation with endogenous proficiency
TLDR
The main idea behind the mechanism is to use the presence of multiple tasks and ratings to estimate a reporting statistic to identify and penalize low-effort agreement, which rewards agents for agreeing with another 'reference' report on the same task, but also penalizes for blind agreement by subtracting out this statistic term. Expand
Minimizing Effective Resistance of a Graph
TLDR
It is shown that optimal allocation of the edge weights can reduce the total effective resistance of the graph (compared to uniform weights) by a factor that grows unboundedly with the size of thegraph. Expand
Selling privacy at auction
TLDR
It is shown that generically, no individually rational mechanism can compensate individuals for the privacy loss incurred due to their reported valuations for privacy, and modeling it correctly is one of the many exciting directions for future work. Expand
Who moderates the moderators?: crowdsourcing abuse detection in user-generated content
TLDR
This paper introduces a framework to address the problem of moderating online content using crowdsourced ratings, and presents efficient algorithms to accurately detect abuse that only require knowledge about the identity of a single 'good' agent, who rates contributions accurately more than half the time. Expand
Selling privacy at auction
TLDR
This work considers a setting in which a data analyst wishes to buy information from a population from which he can estimate some statistic, while the owners of the private data experience some cost for their loss of privacy. Expand
Superposter behavior in MOOC forums
TLDR
It is found that superposters display above-average engagement across Coursera, enrolling in more courses and obtaining better grades than the average forum participant; additionally, students who are super posters in one course are significantly more likely to be superposter in other courses they take. Expand
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