We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for
combinatorial multi-armed bandit problems when no extra information other than the joint …

There are numerous real-world problems where a user must make decisions under
uncertainty. For the problem of influence maximization on a social network, for example, the …

We consider the bandit problem of selecting K out of N arms at each time step. The joint
reward can be a non-linear function of the rewards of the selected individual arms. The …

Learning effective negotiation strategies poses two key challenges: the exploration-
exploitation dilemma and dealing with large action spaces. However, there is an absence of …

This paper investigates the problem of combinatorial multiarmed bandits with stochastic
submodular (in expectation) rewards and full-bandit delayed feedback, where the delayed …

Spot jamming poses a significant threat to radar detection due to its ability to rapidly
intercept radar signals and emit high‐power interference. A novel Cognitive Multi‐Carrier …

In this survey, we offer an extensive overview of the Online Influence Maximization (IM)
problem by covering both theoretical aspects and practical applications. For the integrity of …

Many real-world problems like Social Influence Maximization face the dilemma of choosing
the best $ K $ out of $ N $ options at a given time instant. This setup can be modeled as a …

We consider a combinatorial multi-armed bandit problem for maximum value reward
function under maximum value and index feedback. This is a new feedback structure that …

With an increasing number of users spending time on social media platforms and engaging
with family, friends, and influencers within communities of interest (such as in fashion …