A gradient-free warm-start library learner attains an amortized-regret separation on recurring-regime streams with recognition cost independent of dimension D.
Upper Bounds on the Relative Entropy and R\'enyi Divergence as a Function of Total Variation Distance for Finite Alphabets
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abstract
A new upper bound on the relative entropy is derived as a function of the total variation distance for probability measures defined on a common finite alphabet. The bound improves a previously reported bound by Csisz\'ar and Talata. It is further extended to an upper bound on the R\'enyi divergence of an arbitrary non-negative order (including $\infty$) as a function of the total variation distance.
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Gradient-Free Warm-Start Library Recovery: an Amortized-Regret Separation
A gradient-free warm-start library learner attains an amortized-regret separation on recurring-regime streams with recognition cost independent of dimension D.