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Optimistic Dynamic Regret Bounds
- Publication Year :
- 2023
- Publisher :
- HAL CCSD, 2023.
-
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.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Statistics - Machine Learning
Optimization and Control (math.OC)
FOS: Mathematics
Machine Learning (stat.ML)
Mathematics - Optimization and Control
Machine Learning (cs.LG)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....4ab28507d71803dfa702358a3569de95