Adversarially Robust Submodular Maximization under Knapsack Constraints

Author:

Avdiukhin Dmitrii1,Mitrović Slobodan2,Yaroslavtsev Grigory3,Zhou Samson1

Affiliation:

1. Indiana University, Bloomington, IN, USA

2. Massachusetts Institute of Technology, Cambridge, MA, USA

3. Indiana University & The Alan Turing Institute, Bloomington, IN, USA

Funder

Swiss NSF

Publisher

ACM

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Cost-Efficient Fraud Risk Optimization with Submodularity in Insurance Claim;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Fairness in Streaming Submodular Maximization Subject to a Knapsack Constraint;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

3. Deletion-Robust Submodular Maximization under a Knapsack Constraint;2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2023-12-17

4. Parallel Quasi-Concave Set Function Optimization for Scalability Even Without Submodularity;2023 IEEE High Performance Extreme Computing Conference (HPEC);2023-09-25

5. Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04

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