Fairness in Streaming Submodular Maximization Subject to a Knapsack Constraint-Reference-Cited by-同舟云学术

Fairness in Streaming Submodular Maximization Subject to a Knapsack Constraint

Published:2024-08-24 Issue: Volume:15 Page:514-525
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Cui Shuang¹^ORCID,Han Kai²^ORCID,Tang Shaojie³^ORCID,Li Feng⁴^ORCID,Luo Jun⁵^ORCID

Affiliation:

1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China

2. School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China

3. The University of Texas at Dallas, Richardson, TX, USA

4. Shandong University & Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, Jinan, China

5. Nanyang Technological University, Singapore, Singapore

Funder

Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences)

National Natural Science Foundation of China (NSFC)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671778

Reference79 articles.

1. Georgios Amanatidis Federico Fusco Philip Lazos Stefano Leonardi and Rebecca Reiffenhäuser. 2020. Fast adaptive non-monotone submodular maximization subject to a knapsack constraint. In Advances in Neural Information Processing Systems (NeurIPS).

2. Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint

3. Information-Theoretic Feature Selection via Tensor Decomposition and Submodularity

4. Feature selection using submodular approach for financial big data;Attigeri Girija;Journal of Information Processing Systems,2019

5. Adversarially Robust Submodular Maximization under Knapsack Constraints