Regret in polyhedral online convex optimization equals Θ(√((1+RS_T) T log V_max)) where RS_T counts active region switches.
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Rényi entropy is subadditive on the majorization lattice for every α ∈ [0,∞] and supermodular for α ∈ {0} ∪ [1,∞]; Tsallis entropy is subadditive and supermodular for all α ∈ [0,∞).
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Polyhedral Instability Governs Regret in Online Learning
Regret in polyhedral online convex optimization equals Θ(√((1+RS_T) T log V_max)) where RS_T counts active region switches.
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Geometry of R\'enyi Entropy on the Majorization Lattice
Rényi entropy is subadditive on the majorization lattice for every α ∈ [0,∞] and supermodular for α ∈ {0} ∪ [1,∞]; Tsallis entropy is subadditive and supermodular for all α ∈ [0,∞).