Survey on applications of multi-armed and contextual bandits
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 …
various applications, from recommender systems and information retrieval to healthcare and …
A survey on practical applications of multi-armed and contextual bandits
D Bouneffouf, I Rish - arXiv preprint arXiv:1904.10040, 2019 - arxiv.org
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 …
various applications, from recommender systems and information retrieval to healthcare and …
Dynamic opinion maximization in social networks
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 …
spreading the expected opinions of an object (eg, product or individual) to their neighbors …
Multi-round influence maximization
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 …
influence propagates in multiple rounds independently from possibly different seed sets, and …
Contextual combinatorial bandits with probabilistically triggered arms
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 …
MAB-T) under a variety of smoothness conditions that capture a wide range of applications …
Batch-size independent regret bounds for combinatorial semi-bandits with probabilistically triggered arms or independent arms
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 …
dependency of the batch-size $ K $ in the regret bound, where $ K $ is the total number of …
Improving regret bounds for combinatorial semi-bandits with probabilistically triggered arms and its applications
Q Wang, W Chen - Advances in Neural Information …, 2017 - proceedings.neurips.cc
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 …
and semi-bandit feedback. We resolve a serious issue in the prior CMAB-T studies where …
Deepis: Susceptibility estimation on social networks
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 …
works mainly focus on predicting the total influence spread, ie, the expected number of …
Online influence maximization under linear threshold model
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 …
influence propagation model parameters and maximize the influence spread at the same …
DCM bandits: Learning to rank with multiple clicks
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 …
from the first page to the last, and clicks on all attractive pages until the user is satisfied. This …