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An Improved Uniform Convergence Bound with Fat-Shattering Dimension
The fat-shattering dimension characterizes the uniform convergence property of real-valued functions.
Delayed Bandits: When Do Intermediate Observations Help?
However, if the mapping of states to losses is stochastic, we show that the regret grows at a rate of $\sqrt{\big(K+\min\{|\mathcal{S}|, d\}\big)T}$ (within log factors), implying that if the number $|\mathcal{S}|$ of states is smaller than the delay, then intermediate observations help.
Learning on the Edge: Online Learning with Stochastic Feedback Graphs
The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback.
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