Online Boosting with Bandit Feedback
Nataly Brukhim, Elad Hazan
TL;DR
This paper introduces a novel online boosting algorithm for regression that operates effectively with limited bandit feedback, and also proposes a more efficient projection-free online convex optimization method with stochastic gradients.
Contribution
It presents the first online boosting algorithm capable of handling noisy bandit feedback and introduces a more efficient projection-free online convex optimization technique.
Findings
Achieves low regret with bandit feedback in online boosting.
Improves efficiency of projection-free online convex optimization.
Demonstrates effectiveness through theoretical guarantees.
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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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Optimization and Search Problems · Machine Learning and Algorithms