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Poster
Efficient Contextual Bandits with Continuous Actions
Maryam Majzoubi · Chicheng Zhang · Rajan Chari · Akshay Krishnamurthy · John Langford · Aleksandrs Slivkins
We create a computationally tractable learning algorithm for contextual bandits with continuous actions having unknown structure. The new reduction-style algorithm composes with most supervised learning representations. We prove that this algorithm works in a general sense and verify the new functionality with large-scale experiments.
Author Information
Maryam Majzoubi (NYU)
Chicheng Zhang (University of Arizona)
Rajan Chari (Microsoft)
Akshay Krishnamurthy (Microsoft)
John Langford (Microsoft Research New York)
Alex Slivkins Slivkins (Microsoft Research)
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