Research map / Bandits and online learning / Online convex optimization and games
Strongly Adaptive Online Learning with Time-Varying Movement Cost
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Proposes an online convex optimization algorithm with strongly adaptive regret guarantees over arbitrary intervals when movement costs vary over time.
Problem
Strongly adaptive regret in online convex optimization where both the loss environment and the cost of switching decisions (movement cost) change over time, evaluated on arbitrary sub-intervals.
Approach
An algorithm that, for any interval [a,b] within [T], achieves strongly adaptive regret guarantees that account for time-varying movement costs (the abstract's bound is not included in the provided text).
Experimental setup
Metrics: strongly adaptive regret
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