most citedAdaptive Influence Maximization with Myopic Feedback

12 citations · 22 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2020

Optimization from Structured Samples for Coverage Functions

Wei Chen, Xiaoming Sun, Jialin Zhang +1

We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data. Previous results showed that we can…

cs.LG20204 cited

(Locally) Differentially Private Combinatorial Semi-Bandits

Xiaoyu Chen, Kai Zheng, Zixin Zhou +3

In this paper, we study Combinatorial Semi-Bandits (CSB) that is an extension of classic Multi-Armed Bandits (MAB) under Differential Privacy (DP) and stronger Local Differential P…

cs.SI20203 cited

Efficient Approximation Algorithms for Adaptive Influence Maximization

Keke Huang, Jing Tang, Kai Han +5

Given a social network and an integer , the influence maximization (IM) problem asks for a seed set of nodes from to maximize the expected number of nodes influe…

math.OC2019

Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations

Wei Chen, Weizhong Zhang, Haoyu Zhao

Continuous influence maximization (CIM) generalizes the original influence maximization by incorporating general marketing strategies: a marketing strategy mix is a vector $\boldsy…

cs.LG20193 cited

Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting

Haoyu Zhao, Wei Chen

In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in rounds by the second price auctio…

cs.SI2019

Influence Maximization with Spontaneous User Adoption

Lichao Sun, Albert Chen, Philip S. Yu +1

We incorporate self activation into influence propagation and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as se…