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Found 4 papers, 0 papers with code
Combinatorial Stochastic-Greedy Bandit
We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the selected set of $n$ arms at each time step $t\in [T]$ is observed.
A Unified Approach for Maximizing Continuous DR-submodular Functions
This paper presents a unified approach for maximizing continuous DR-submodular functions that encompasses a range of settings and oracle access types.
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback
We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization.
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback
The framework only requires the offline algorithms to be robust to small errors in function evaluation.
