Submodular functions and convexity

László Lovász · 1983 · DOI 10.1007/978-3-642-68874-4_10

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cs.LG · 2026-05-13 · conditional · novelty 7.0

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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Polyhedral Instability Governs Regret in Online Learning cs.LG · 2026-05-13 · conditional · none · ref 15
Regret in polyhedral online convex optimization equals Θ(√((1+RS_T) T log V_max)) where RS_T counts active region switches.