activity
20172026
most citedCampus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene

57 citations · 107 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.DS2026

Data-dependent Evaluations for Budgeted Submodular Maximization

Lejian Zhang, Xueyan Tang, Jing Tang

Submodular maximization is an important building block for developing algorithms in many areas such as machine learning and data mining. Due to the NP-hardness of the problem, anal…

cs.DS2023

Practical Parallel Algorithms for Non-Monotone Submodular Maximization

Shuang Cui, Kai Han, Jing Tang +3

Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vis…

cs.DS2023

Efficient Approximation Algorithms for Spanning Centrality

Shiqi Zhang, Renchi Yang, Jing Tang +2

Given a graph , the spanning centrality (SC) of an edge measures the importance of for to be connected. In practice, SC has seen extensive applic…

cs.DS2021

Efficient and Effective Algorithms for Revenue Maximization in Social Advertising

Kai Han, Benwei Wu, Jing Tang +3

We consider the revenue maximization problem in social advertising, where a social network platform owner needs to select seed users for a group of advertisers, each with a payment…

cs.DS2021

The Power of Randomization: Efficient and Effective Algorithms for Constrained Submodular Maximization

Kai Han, Shuang Cui, Tianshuai Zhu +3

Submodular optimization has numerous applications such as crowdsourcing and viral marketing. In this paper, we study the fundamental problem of non-negative submodular function max…

cs.DS2020

Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint

Jing Tang, Xueyan Tang, Andrew Lim +3

Monotone submodular maximization with a knapsack constraint is NP-hard. Various approximation algorithms have been devised to address this optimization problem. In this paper, we r…