Online combinatorial optimization with stochastic decision sets and adversarial losses

NeurIPS 2014 Gergely NeuMichal Valko

Most work on sequential learning assumes a fixed set of actions that are available all the time. However, in practice, actions can consist of picking subsets of readings from sensors that may break from time to time, road segments that can be blocked or goods that are out of stock... (read more)

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