mlsanityData to 14 Oct 2026

Decomposable Submodular Function Minimization: Discrete and Continuous

arXiv 1703.01830 ↗First posted 6 Mar 2017cs.LG cs.DScited by 5

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Abstract (arXiv)

This paper investigates connections between discrete and continuous approaches for decomposable submodular function minimization. We provide improved running time estimates for the state-of-the-art continuous algorithms for the problem using combinatorial arguments. We also provide a systematic experimental comparison of the two types of methods, based on a clear distinction between level-0 and level-1 algorithms.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Mar 2017.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 1.

5 papers.

  1. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 82
  2. 2023Heterogeneous Decentralized Machine Unlearning with Seed Model Distillationcited by 4
  3. 2021Decomposable Submodular Function Minimization via Maximum Flowcited by 3
  4. 2022Sparsification of Decomposable Submodular Functionscited by 3
  5. 2021Approximate Decomposable Submodular Function Minimization for Cardinality-Based Componentscited by 1