Optimistic Dynamic Regret Bounds
Abstract: Online Learning (OL) algorithms have originally been developed to guarantee good performances when comparing their output to the best fixed strategy. The question of performance with respect to dynamic strategies remains an active research topic. We develop in this work dynamic adaptations of classical OL algorithms based on the use of experts' advice and the notion of optimism. We also propose a constructivist method to generate those advices and eventually provide both theoretical and experimental guarantees for our procedures.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: We updated our work according to the reviewers comments. All associated changes are written in red for a more efficient discussion phase.
Assigned Action Editor: ~Nishant_A_Mehta1
Submission Number: 782
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