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Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
We derive concentration bounds for CVaR estimates, considering separately the cases of light-tailed and heavy-tailed distributions.
Concentration bounds for empirical conditional value-at-risk: The unbounded case
In several real-world applications involving decision making under uncertainty, the traditional expected value objective may not be suitable, as it may be necessary to control losses in the case of a rare but extreme event.
Bandit algorithms to emulate human decision making using probabilistic distortions
For the $K$-armed bandit setting, we derive an upper bound on the expected regret for our proposed algorithm, and then we prove a matching lower bound to establish the order-optimality of our algorithm.
Collaborative Learning of Stochastic Bandits over a Social Network
A key finding of this paper is that natural extensions of widely-studied single agent learning policies to the network setting need not perform well in terms of regret.
