Introduces a distributed stochastic setting for graph optimization and supplies fast approximation algorithms for matching, vertex cover, and dominating set that surpass non-stochastic lower bounds.
Papadimitriou and Kenneth Steiglitz , title =
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Random feature selection outperforms many state-of-the-art unsupervised feature selection methods in both performance and efficiency.
citing papers explorer
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Distributed Stochastic Graph Algorithms
Introduces a distributed stochastic setting for graph optimization and supplies fast approximation algorithms for matching, vertex cover, and dominating set that surpass non-stochastic lower bounds.
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Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection
Random feature selection outperforms many state-of-the-art unsupervised feature selection methods in both performance and efficiency.