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cs.DS2026
Improved Guarantees for Offline Stochastic Matching via New Ordered Contention Resolution Schemes
Brian Brubach, Nathaniel Grammel, Will Ma +2
Matching is one of the most fundamental and broadly applicable problems across many domains. In these diverse real-world applications, there is often a degree of uncertainty in the…
cs.DS2024
Dependent randomized rounding for clustering and partition systems with knapsack constraints
David G. Harris, Thomas Pensyl, Aravind Srinivasan +1
Clustering problems are fundamental to unsupervised learning. There is an increased emphasis on fairness in machine learning and AI; one representative notion of fairness is that n…
cs.DS2024
Stochastic Optimization and Learning for Two-Stage Supplier Problems
Brian Brubach, Nathaniel Grammel, David G. Harris +3
The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the for…