Uncertainty in Artificial Intelligence
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Censored Exploration and the Dark Pool Problem
Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan
We introduce and analyze a natural algorithm for multi-venue exploration from censored data, which is motivated by the Dark Pool Problem of modern quantitative finance. We prove that our algorithm converges in polynomial time to a near-optimal allocation policy; prior results for similar problems in stochastic inventory control guaranteed only asymptotic convergence and examined variants in which each venue could be treated independently. Our analysis bears a strong resemblance to that of efficient exploration/ exploitation schemes in the reinforcement learning literature. We describe an extensive experimental evaluation of our algorithm on the Dark Pool Problem using real trading data.
Keywords: null
Pages: 185-194
PS Link:
PDF Link: /papers/09/p185-ganchev.pdf
AUTHOR = "Kuzman Ganchev and Michael Kearns and Yuriy Nevmyvaka and Jennifer Wortman Vaughan",
TITLE = "Censored Exploration and the Dark Pool Problem",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "185--194"

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