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A Modern Introduction to Online Learning
[article]
2022
arXiv
pre-print
In this monograph, I introduce the basic concepts of Online Learning through a modern view of Online Convex Optimization. Here, online learning refers to the framework of regret minimization under worst-case assumptions. I present first-order and second-order algorithms for online learning with convex losses, in Euclidean and non-Euclidean settings. All the algorithms are clearly presented as instantiation of Online Mirror Descent or Follow-The-Regularized-Leader and their variants. Particular
arXiv:1912.13213v5
fatcat:2jfr62y6ofg5boqzv3a6mybgo4