157 citations · 713 across the 56 of their papers we have counts for
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cs.LG2015★ 18 cited
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
Gergely Neu
This work addresses the problem of regret minimization in non-stochastic multi-armed bandit problems, focusing on performance guarantees that hold with high probability. Such resul…
cs.LG2015★ 10 cited
Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits
Gergely Neu, Gábor Bartók
We propose a sample-efficient alternative for importance weighting for situations where one only has sample access to the probability distribution that generates the observations.…
cs.LG2015★ 14 cited
First-order regret bounds for combinatorial semi-bandits
Gergely Neu
We consider the problem of online combinatorial optimization under semi-bandit feedback, where a learner has to repeatedly pick actions from a combinatorial decision set in order t…