Proceedings of the 2018 Workshop on Network Meets AI & ML - NetAI'18 2018
DOI: 10.1145/3229543.3229551
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Abstract: Bandit Convex Optimisation (BCO) is a powerful framework for sequential decision-making in non-stationary and partially observable environments. In a BCO problem, a decisionmaker sequentially picks actions to minimize the cumulative cost associated with these decisions, all while receiving partial feedback about the state of the environment. This formulation is a very natural fit for wireless-network optimisation problems and has great application potential since: i) instead of assuming full observability of …

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