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Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
A key challenge in science and engineering is to design experiments to learn about some unknown quantity of interest. Classical experimental design optimally allocates the experimental budget into measurements to maximize a notion of utility (e.g., reduction in uncertainty about the unknown quantity). We consider a rich setting, where the experiments are associated with states in a Markov chain, and we can only choose them by selecting a policy controlling the state transitions. This problem captures important applications, from exploration in reinforcement learning to spatial monitoring tasks. We propose an algorithm – markov-design – that efficiently selects policies whose measurement allocation provably converges to the optimal one. The algorithm is sequential in nature, adapting its choice of policies (experiments) using past measurements. In addition to our theoretical analysis, we demonstrate our framework on applications in ecological surveillance and pharmacology. Show more
Publication status
publishedExternal links
Book title
Proceedings of The 26th International Conference on Artificial Intelligence and StatisticsJournal / series
Proceedings of Machine Learning ResearchVolume
Pages / Article No.
Publisher
PMLREvent
Organisational unit
03908 - Krause, Andreas / Krause, Andreas
Funding
180544 - NCCR Catalysis (phase I) (SNF)
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ETH Bibliography
yes
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