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Maximization of submodular functions has wide applications in machine learning and artificial intelligence. Adaptive submodular maximization has been traditionally studied under the assumption that the model of the world, the expected gain of choosing an item given previously selected items and their states, is known. In this paper, we study the setting where the expected gain is initially unknown, and it is learned by interacting repeatedly with the optimized function. We propose an efficientdblp:conf/nips/GabillonKWEM13 fatcat:5mgjdxmpxfg4bfbuhbowx67pum