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A Second-Order Method for Stochastic Bandit Convex Optimisation (2302.05371v1)
Published 10 Feb 2023 in cs.LG, math.OC, and stat.ML
Abstract: We introduce a simple and efficient algorithm for unconstrained zeroth-order stochastic convex bandits and prove its regret is at most $(1 + r/d)[d{1.5} \sqrt{n} + d3] polylog(n, d, r)$ where $n$ is the horizon, $d$ the dimension and $r$ is the radius of a known ball containing the minimiser of the loss.
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