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Differentially Private Online Submodular Maximization
[article]
2020
arXiv
pre-print
In this work we consider the problem of online submodular maximization under a cardinality constraint with differential privacy (DP). A stream of T submodular functions over a common finite ground set U arrives online, and at each time-step the decision maker must choose at most k elements of U before observing the function. The decision maker obtains a payoff equal to the function evaluated on the chosen set, and aims to learn a sequence of sets that achieves low expected regret. In the
arXiv:2010.12816v1
fatcat:jbciay4wxregjf6csjkpg7ouiu