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Semiparametric Contextual Bandits
Akshay Krishnamurthy · Steven Wu · Vasilis Syrgkanis

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #136
This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for a chosen action is modeled as a linear function of known action features confounded by a non-linear action-independent term. We design new algorithms that achieve $\tilde{O}(d\sqrt{T})$ regret over $T$ rounds, when the linear function is $d$-dimensional, which matches the best known bounds for the simpler unconfounded case and improves on a recent result of Greenwald et al. (2017). Via an empirical evaluation, we show that our algorithms outperform prior approaches when there are non-linear confounding effects on the rewards. Technically, our algorithms use a new reward estimator inspired by doubly-robust approaches and our proofs require new concentration inequalities for self-normalized martingales.

Author Information

Akshay Krishnamurthy (Microsoft Research)
Steven Wu (Microsoft Research)
Vasilis Syrgkanis (Microsoft Research)

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