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We consider the neural contextual bandit problem. In contrast to the existingwork which primarily focuses on ReLU neural nets, we consider a general set ofsmooth activation functions. Under this more general setting, (i) we derivenon-asymptotic error bounds on the difference between an overparameterizedneural net and its corresponding neural tangent kernel, (ii) we propose analgorithm with a provably sublinear regret bound that is also efficient in thefinite regime as demonstrated by empirical studies. The non-asymptotic errorbounds may be of broader interest as a tool to establish the relation betweenthe smoothness of the activation functions in neural contextual bandits and thesmoothness of the kernels in kernel bandits.

Provably and Practically Efficient Neural Contextual Bandits