We provide the first provable approximation guarantees for efficient algorithms for the optimization problem of selecting the most influential nodes in several of the most widely studied social network models.Expand

We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest.Expand

We study the problem of pricing items for sale to consumers so as to maximize the seller's revenue by finding envy-free prices that maximize seller profit and at the same time are envy free.Expand

We study the problem of maximizing the expected spread of an innovation or behavior within a social network, in the presence of “word-of-mouth” referral.Expand

We study the problem of selecting a subset of k random variables to observe that will yield the best linear prediction of another variable of interest, given the pairwise correlations between the observation variables and the predictor variable.Expand

We consider situations in which a decision-maker with a fixed budget faces a sequence of options, each with a cost and a value, and must select a subset of them online so as to maximize the total value.Expand