Researchers propose ATCG, an improved algorithm for submodular maximization under matroid constraints that reduces communication overhead in distributed settings. The method adaptively gates gradient evaluations to bound feature embedding exchanges while maintaining near-optimal approximation guarantees. Applicable to sensing, data summarization, active learning, and resource allocation tasks.
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Adaptive Threshold-Driven Continuous Greedy Method for Scalable Submodular Optimization
ATCG algorithm slashes communication costs in distributed submodular optimization by adaptively gating gradient evaluations, enabling efficient data summarization and resource allocation at scale.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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