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Online Boosting with Bandit Feedback
Published 23 Jul 2020 in cs.LG and stat.ML | (2007.11975v1)
Abstract: We consider the problem of online boosting for regression tasks, when only limited information is available to the learner. We give an efficient regret minimization method that has two implications: an online boosting algorithm with noisy multi-point bandit feedback, and a new projection-free online convex optimization algorithm with stochastic gradient, that improves state-of-the-art guarantees in terms of efficiency.
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