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Poster
High-dimensional Experimental Design and Kernel Bandits
Romain Camilleri · Kevin Jamieson · Julian Katz-Samuels

Wed Jul 21 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 3 »

In recent years methods from optimal linear experimental design have been leveraged to obtain state of the art results for linear bandits. A design returned from an objective such as G-optimal design is actually a probability distribution over a pool of potential measurement vectors. Consequently, one nuisance of the approach is the task of converting this continuous probability distribution into a discrete assignment of N measurements. While sophisticated rounding techniques have been proposed, in d dimensions they require N to be at least d, d log(log(d)), or d^2 based on the sub-optimality of the solution. In this paper we are interested in settings where N may be much less than d, such as in experimental design in an RKHS where d may be effectively infinite.
In this work, we propose a rounding procedure that frees N of any dependence on the dimension d, while achieving nearly the same performance guarantees of existing rounding procedures. We evaluate the procedure against a baseline that projects the problem to a lower dimensional space and performs rounding there, which requires N to just be at least a notion of the effective dimension. We also leverage our new approach in a new algorithm for kernelized bandits to obtain state of the art results for regret minimization and pure exploration. An advantage of our approach over existing UCB-like approaches is that our kernel bandit algorithms are provably robust to model misspecification.

Keywords: Bandits · Reinforcement Learning and Planning

Author Information

Romain Camilleri (University of Washington)
Kevin Jamieson (University of Washington)
Julian Katz-Samuels (University of Wisconsin-Madison)

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