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New algorithm offers adversarial resilience for submodular maximization

By PulseAugur Editorial · [1 sources] ·

This research paper introduces a new algorithm called the Spiteful Greedy Swap Poisson Process (SGS-Poisson) for submodular maximization problems with matroid constraints. The algorithm demonstrates adversarial resilience, meaning it maintains its approximation factors even when faced with imperfect value oracles. This resilience allows for the development of full-bandit contextual multi-armed bandit (CMAB) algorithms with improved regret bounds for submodular rewards. AI

IMPACT Introduces theoretical advancements in optimization algorithms relevant to machine learning research.

RANK_REASON The cluster contains a single academic paper detailing a new algorithm and theoretical results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm offers adversarial resilience for submodular maximization

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The cluster contains a single academic paper detailing a new algorithm and theoretical results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Vaneet Aggarwal ·

    Adversarial Resilience of Poisson-Process Submodular Maximization over Matroids: From Robust Offline Optimization to Full-Bandit Learning

    arXiv:2608.12134v1 Announce Type: cross Abstract: We study nonnegative submodular maximization subject to a general matroid when the offline algorithm is given an arbitrary controlled value oracle. Our main result is an adversarial resilience theorem for the Spiteful Greedy Swap …

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