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We address the problem of maximizing an unknown submodular function that canonly be accessed via noisy evaluations. Our work is motivated by the task ofsummarizing content, e.g., image collections, by leveraging users' feedback inform of clicks or ratings. For summarization tasks with the goal of maximizingcoverage and diversity, submodular set functions are a natural choice. When theunderlying submodular function is unknown, users' feedback can provide noisyevaluations of the function that we seek to maximize. We provide a genericalgorithm -- \submM{} -- for maximizing an unknown submodular function undercardinality constraints. This algorithm makes use of a novel exploration module-- \blbox{} -- that proposes good elements based on adaptively sampling noisyfunction evaluations. \blbox{} is able to accommodate different kinds ofobservation models such as value queries and pairwise comparisons. We providePAC-style guarantees on the quality and sampling cost of the solution obtainedby \submM{}. We demonstrate the effectiveness of our approach in aninteractive, crowdsourced image collection summarization application.

Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization