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One-Shot Session Recommendation Systems with Combinatorial Items
Our optimization problem is formulated as an MDP where the action space is of a combinatorial nature as we recommend in each round, multiple items.
The Max $K$-Armed Bandit: PAC Lower Bounds and Efficient Algorithms
Under the PAC framework, we provide a lower bound on the sample complexity of any $(\epsilon,\delta)$-correct algorithm, and propose an algorithm that attains this bound up to logarithmic factors.
The Max $K$-Armed Bandit: A PAC Lower Bound and tighter Algorithms
We consider the Max $K$-Armed Bandit problem, where a learning agent is faced with several sources (arms) of items (rewards), and interested in finding the best item overall.
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