mlsanityData to 7 Oct 2026

Predictive Flows for Faster Ford-Fulkerson

arXiv 2303.00837 ↗First posted 1 Mar 2023cs.DScited by 20

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Abstract (arXiv)

Recent work has shown that leveraging learned predictions can improve the running time of algorithms for bipartite matching and similar combinatorial problems. In this work, we build on this idea to improve the performance of the widely used Ford-Fulkerson algorithm for computing maximum flows by seeding Ford-Fulkerson with predicted flows. Our proposed method offers strong theoretical performance in terms of the quality of the prediction. We then consider image segmentation, a common use-case of flows in computer vision, and complement our theoretical analysis with strong empirical results.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 5; 2025: 6; 2026: 5.

20 papers.

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  1. 2023Sorting with Predictionscited by 14
  2. 2024Binary Search with Distributional Predictionscited by 11
  3. 2023Online List Labeling with Predictionscited by 5
  4. 2023The Predicted-Updates Dynamic Model: Offline, Incremental, and Decremental to Fully Dynamic Transformationscited by 4
  5. 2024Learning-Augmented Search Data Structurescited by 4
  6. 2024Competitive strategies to use "warm start" algorithms with predictionscited by 4
  7. 2023Faster Discrete Convex Function Minimization with Predictions: The M-Convex Casecited by 3
  8. 2025Incremental Approximate Single-Source Shortest Paths with Predictionscited by 3
  9. 2024Warm-starting Push-Relabelcited by 2
  10. 2025Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictorscited by 2
  11. 2024On Approximability of ℓ22 Min-Sum Clusteringcited by 1
  12. 2025Learning-Augmented Streaming Algorithms for Correlation Clusteringcited by 1
8 more
  1. 2025Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulationcited by 0
  2. 2025Transductive and Learning-Augmented Online Regressioncited by 0
  3. 2025Neural Tractability via Structure: Learning-Augmented Algorithms for Graph Combinatorial Optimizationcited by 0
  4. 2026Minimum-Cost Network Flow with Dual Predictionscited by 0
  5. 2026Better Learning-Augmented Spanning Tree Algorithms via Metric Forest Completioncited by 0
  6. 2026Graph Neural Network-Informed Predictive Flows for Faster Ford-Fulkerson and PAC-Learnabilitycited by 0
  7. 2026Incremental Strongly Connected Components with Predictionscited by 0
  8. 2026Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implicationscited by 0