936 research outputs found
Jointly Optimal Routing and Caching for Arbitrary Network Topologies
We study a problem of fundamental importance to ICNs, namely, minimizing
routing costs by jointly optimizing caching and routing decisions over an
arbitrary network topology. We consider both source routing and hop-by-hop
routing settings. The respective offline problems are NP-hard. Nevertheless, we
show that there exist polynomial time approximation algorithms producing
solutions within a constant approximation from the optimal. We also produce
distributed, adaptive algorithms with the same approximation guarantees. We
simulate our adaptive algorithms over a broad array of different topologies.
Our algorithms reduce routing costs by several orders of magnitude compared to
prior art, including algorithms optimizing caching under fixed routing.Comment: This is the extended version of the paper "Jointly Optimal Routing
and Caching for Arbitrary Network Topologies", appearing in the 4th ACM
Conference on Information-Centric Networking (ICN 2017), Berlin, Sep. 26-28,
201
Truthful Linear Regression
We consider the problem of fitting a linear model to data held by individuals
who are concerned about their privacy. Incentivizing most players to truthfully
report their data to the analyst constrains our design to mechanisms that
provide a privacy guarantee to the participants; we use differential privacy to
model individuals' privacy losses. This immediately poses a problem, as
differentially private computation of a linear model necessarily produces a
biased estimation, and existing approaches to design mechanisms to elicit data
from privacy-sensitive individuals do not generalize well to biased estimators.
We overcome this challenge through an appropriate design of the computation and
payment scheme.Comment: To appear in Proceedings of the 28th Annual Conference on Learning
Theory (COLT 2015
Instantaneous vehicle fuel consumption estimation using smartphones and Recurrent Neural Networks
Learning Mixtures of Linear Classifiers
We consider a discriminative learning (regression) problem, whereby the
regression function is a convex combination of k linear classifiers. Existing
approaches are based on the EM algorithm, or similar techniques, without
provable guarantees. We develop a simple method based on spectral techniques
and a `mirroring' trick, that discovers the subspace spanned by the
classifiers' parameter vectors. Under a probabilistic assumption on the feature
vector distribution, we prove that this approach has nearly optimal statistical
efficiency
COMPENSATING VARIATION FOR RECREATIONAL POLICY: A RANDOM UTILITY APPROACH TO BOATING IN FLORIDA
A nested logit random utility travel cost model is developed for recreational boating in southwest Florida. Using data from a survey of recreational boaters, the model estimates site choice probabilities and compensating variation for changes in boating speed limits. Behavior is modeled as a two-step, discrete-choice process, where boaters first select a launch point for their trailered boats, then select a boating destination based on site characteristics. The results of this particular model are currently being used in policy applications in Florida.Resource /Energy Economics and Policy,
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