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Found 6 papers, 1 papers with code
Adaptive Submodular Ranking and Routing
We obtain a logarithmic factor approximation algorithm for this adaptive ranking problem, which is the best possible (unless P=NP).
Batched Dueling Bandits
The $K$-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied.
Minimum Cost Adaptive Submodular Cover
We consider the problem of minimum cost cover of adaptive-submodular functions, and provide a 4(ln Q+1)-approximation algorithm, where Q is the goal value.
An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem
}$ We answer this in the affirmative $\textit{under the Condorcet condition}$, a standard setting of the $K$-armed dueling bandit problem.
Optimal Decision Tree with Noisy Outcomes
In pool-based active learning, the learner is given an unlabeled data set and aims to efficiently learn the unknown hypothesis by querying the labels of the data points.
Semi-Bandit Learning for Monotone Stochastic Optimization
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.
