V
Vahab Mirrokni
Researcher at Google
Publications - 390
Citations - 16175
Vahab Mirrokni is an academic researcher from Google. The author has contributed to research in topics: Computer science & Common value auction. The author has an hindex of 57, co-authored 346 publications receiving 14255 citations. Previous affiliations of Vahab Mirrokni include Vassar College & Microsoft.
Papers
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Proceedings ArticleDOI
Locality-sensitive hashing scheme based on p-stable distributions
TL;DR: A novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions that improves the running time of the earlier algorithm and yields the first known provably efficient approximate NN algorithm for the case p<1.
Journal ArticleDOI
Maximizing Non-monotone Submodular Functions
TL;DR: This paper designs the first constant-factor approximation algorithms for maximizing nonnegative (non-monotone) submodular functions and proves NP- hardness of $(\frac{5}{6}+\epsilon)$-approximation in the symmetric case and NP-hardness of $\frac{3}{4}+ \epsil on)$ in the general case.
Proceedings ArticleDOI
Optimal marketing strategies over social networks
TL;DR: This work identifies a family of strategies called influence-and-exploit strategies that are based on the following idea: Initially influence the population by giving the item for free to carefully a chosen set of buyers, then extract revenue from the remaining buyers using a 'greedy' pricing strategy.
Proceedings ArticleDOI
Online Stochastic Matching: Beating 1-1/e
TL;DR: In this article, the authors study the online stochastic bipartite matching problem, in a form motivated by display ad allocation on the Internet, and show that no online algorithm can achieve an approximation ratio better than 0.632.
Proceedings ArticleDOI
Trust-based recommendation systems: an axiomatic approach
Reid Andersen,Christian Borgs,Jennifer Chayes,Uriel Feige,Abraham D. Flaxman,Adam Tauman Kalai,Vahab Mirrokni,Moshe Tennenholtz +7 more
TL;DR: Which of these networks that represent trust and recommendation systems that incorporate these trust relationships are incentive compatible are determined, meaning that groups of agents interested in manipulating recommendations can not induce others to share their opinion by lying about their votes or modifying their trust links.