Unified high-probability regret bounds for online convex optimisation with ℓq-Lipschitz losses via ℓp-regularised FTRL and cone-measure sampling from ℓr-spheres, for all p,q,r in [1,∞].
Proposition E.2.Suppose Assumption 3.2 holds
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High-probability zeroth-order online convex optimisation beyond Euclidean geometry
Unified high-probability regret bounds for online convex optimisation with ℓq-Lipschitz losses via ℓp-regularised FTRL and cone-measure sampling from ℓr-spheres, for all p,q,r in [1,∞].