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Tighter Confidence Bounds for Sequential Kernel Regression
In this capacity, they can inform the exploration-exploitation trade-off and form a core component in many sequential learning and decision-making algorithms.
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
On the one hand, we found that PAC-Bayes bounds are a useful tool for designing offline bandit algorithms with performance guarantees.
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
We present a PAC-Bayesian analysis of lifelong learning.
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