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This paper presents an online algorithm for maximizing non-monotone DR-submodular functions under down-closed convex constraints, achieving the best-known offline approximation factor of 0.401 with sublinear regret in the full-information value-oracle model.
This paper presents a unified algorithmic framework for distributed online submodular maximization under partition matroid constraints, achieving sublinear (1-1/e)-regret guarantees for both full-information and bandit feedback. It also introduces a bounded stochastic pipage rounding scheme to ensure cumulative sampling violations remain sublinear.
This paper introduces answer-in-context, a diagnostic metric for budget-constrained multi-hop RAG that measures whether the gold answer survives in the packed reader context, and proposes a submodular evidence packing method that improves over heuristics under specific conditions.
This paper introduces CDPR, a new approach for learning highly accurate and interpretable rule sets for classification using submodular maximization, achieving over 2.5x improvement in coverage compared to existing methods.