AI RESEARCH
Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memory
arXiv CS.LG
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ArXi:2602.06902v2 Announce Type: replace In this paper, we study dynamic regret in unconstrained online convex optimization (OCO) with movement costs. Specifically, we generalize the standard setting by allowing the movement cost coefficients $\lambda_t$ to vary arbitrarily over time.