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Locality-sensitive hashing scheme based on p-stable distributions
TLDR
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.
Maximizing Non-Monotone Submodular Functions
Submodular maximization generalizes many important problems including Max Cut in directed/undirected graphs and hypergraphs, certain constraint satisfaction problems and maximum facility location
Online Stochastic Matching: Beating 1-1/e
TLDR
A novel application of the idea of the power of two choices from load balancing, which compute two disjoint solutions to the expected instance, and use both of them in the online algorithm in a prescribed preference order to characterize an upper bound for the optimum in any scenario.
Approximating submodular functions everywhere
TLDR
The problem of approximating a non-negative, monotone, submodular function f on a ground set of size n everywhere is considered, after only poly(n) oracle queries, and it is shown that no algorithm can achieve a factor better than Ω(√n/log n), even for rank functions of a matroid.
Stochastic bandits robust to adversarial corruptions
We introduce a new model of stochastic bandits with adversarial corruptions which aims to capture settings where most of the input follows a stochastic pattern but some fraction of it can be
Optimal marketing strategies over social networks
TLDR
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.
Non-monotone submodular maximization under matroid and knapsack constraints
TLDR
This paper gives the first constant-factor approximation algorithm for maximizing any non-negative submodular function subject to multiple matroid or knapsack constraints, and improves the approximation guarantee of the algorithm to 1/k+1+{1/k-1}+ε for k≥2 partition matroid constraints.
Sink equilibria and convergence
TLDR
It is argued that there is a natural convergence process to sink equilibria in games where agents use pure strategies, and the price of sinking is an alternative measure of the social cost of a lack of coordination, which measures the worst case ratio between thevalue of a sink equilibrium and the value of the socially optimal solution.
Online Ad Assignment with Free Disposal
We study an online weighted assignment problem with a set of fixed nodes corresponding to advertisers and online arrival of nodes corresponding to ad impressions. Advertiser a has a contract for n(a)
Tight approximation algorithms for maximum general assignment problems
TLDR
The (1 - 1/e)-approximation algorithm is extended to a nonseparable assignment problem with applications in maximizing revenue for budget-constrained combinatorial auctions and the AdWords assignment problem and the existence of cycles of best response moves, and exponentially long best-response paths to (pure or sink) equilibria is proved.
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