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Variational Dropout and the Local Reparameterization Trick
We explore an as yet unexploited opportunity for drastically improving the efficiency of stochastic gradient variational Bayes (SGVB) with global model parameters. Regular SGVB estimators rely onExpand
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Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries
The stochastic matching problem deals with finding a maximum matching in a graph whose edges are unknown but can be accessed via queries. This is a special case of stochastic k-set packing, where theExpand
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Commitment Without Regrets: Online Learning in Stackelberg Security Games
In a Stackelberg Security Game, a defender commits to a randomized deployment of security resources, and an attacker best-responds by attacking a target that maximizes his utility. While algorithmsExpand
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Efficient Learning of Linear Separators under Bounded Noise
We study the learnability of linear separators in $\Re^d$ in the presence of bounded (a.k.a Massart) noise. This is a realistic generalization of the random classification noise model, where theExpand
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Learning Optimal Commitment to Overcome Insecurity
Game-theoretic algorithms for physical security have made an impressive real-world impact. These algorithms compute an optimal strategy for the defender to commit to in a Stackelberg game, where theExpand
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k-Center Clustering Under Perturbation Resilience
The $k$-center problem is a canonical and long-studied facility location and clustering problem with many applications in both its symmetric and asymmetric forms. Both versions of the problem haveExpand
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Collaborative PAC Learning
We introduce a collaborative PAC learning model, in which k players attempt to learn the same underlying concept. We ask how much more information is required to learn an accurate classifier for allExpand
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Symmetric and Asymmetric $k$-center Clustering under Stability
The k-center problem is a canonical and long-studied facility location and clustering problem with many applications in both its symmetric and asymmetric forms. Both versions of the problem haveExpand
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Learning and 1-bit Compressed Sensing under Asymmetric Noise
We study the approximate recovery problem under noise: Given corrupted 1-bit measurements of the form sign(w∗ · xi), recover a vector w with a small 0/1 loss w.r.t. w∗ ∈ R. In learning theory, thisExpand
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