Provably and Practically Efficient Neural Contextual Bandits

Understand

We consider the neural contextual bandit problem.

• In contrast to the existing work which primarily focuses on ReLU neural nets, we consider a general set of smooth activation functions.
• Under this more general setting, (i) we derive non-asymptotic error bounds on the difference between an overparameterized neural net and its corresponding neural tangent kernel, (ii) we propose an algorithm with a provably sublinear regret bound that is also efficient in the finite regime as demonstrated by empirical studies.
• The non-asymptotic error bounds may be of broader interest as a tool to establish the relation between the smoothness of the activation functions in neural contextual bandits and the smoothness of the kernels in kernel bandits.