mlsanityData to 14 Oct 2026

Streaming Adaptive Submodular Maximization

arXiv 2208.08021 ↗First posted 17 Aug 2022cs.AI cs.LGcited by 2

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Abstract (arXiv)

Many sequential decision making problems can be formulated as an adaptive submodular maximization problem. However, most of existing studies in this field focus on pool-based setting, where one can pick items in any order, and there have been few studies for the stream-based setting where items arrive in an arbitrary order and one must immediately decide whether to select an item or not upon its arrival. In this paper, we introduce a new class of utility functions, semi-policywise submodular functions. We develop a series of effective algorithms to maximize a semi-policywise submodular function under the stream-based setting.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 17 Aug 2022.

Builds on

10 of 20 references.

References · 10

  1. 2019Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexitycited by 14
  2. 2020Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization in Linear Timecited by 12
  3. 2021Optimal Sampling Gaps for Adaptive Submodular Maximizationcited by 9
  4. 2021Adaptive Regularized Submodular Maximizationcited by 8
  5. 2021Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization subject to Knapsack and k-System Constraintscited by 8
  6. 2020Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Timecited by 7
  7. 2016Combinatorial Prophet Inequalitiescited by 4
  8. 2021Robust Adaptive Submodular Maximizationcited by 4
  9. 2020Submodular Meta-Learningcited by 2
  10. 2021On Submodular Prophet Inequalities and Correlation Gapcited by 2

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.