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Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits
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.
Efficient Partial Monitoring with Prior Information
In every round, the learner suffers some loss and receives some feedback based on the action and the outcome.
Near-Optimally Teaching the Crowd to Classify
How should we present training examples to learners to teach them classification rules?
An efficient algorithm for learning with semi-bandit feedback
We consider the problem of online combinatorial optimization under semi-bandit feedback.
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