In recent years, the multi-armed bandit (MAB) framework has attracted a lot of attention in
various applications, from recommender systems and information retrieval to healthcare and …

In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in
various applications, from recommender systems and information retrieval to healthcare and …

Opinion Maximization (OM) aims at determining a small set of influential individuals,
spreading the expected opinions of an object (eg, product or individual) to their neighbors …

In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where
influence propagates in multiple rounds independently from possibly different seed sets, and …

We study contextual combinatorial bandits with probabilistically triggered arms (C $^ 2$
MAB-T) under a variety of smoothness conditions that capture a wide range of applications …

In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the
dependency of the batch-size $ K $ in the regret bound, where $ K $ is the total number of …

We study combinatorial multi-armed bandit with probabilistically triggered arms (CMAB-T)
and semi-bandit feedback. We resolve a serious issue in the prior CMAB-T studies where …

Influence diffusion estimation is a crucial problem in social network analysis. Most prior
works mainly focus on predicting the total influence spread, ie, the expected number of …

Online influence maximization (OIM) is a popular problem in social networks to learn
influence propagation model parameters and maximize the influence spread at the same …

A search engine recommends to the user a list of web pages. The user examines this list,
from the first page to the last, and clicks on all attractive pages until the user is satisfied. This …