mlsanityData to 7 Oct 2026

Improved Approximations for Hard Graph Problems using Predictions

arXiv 2505.23967 ↗First posted 29 May 2025cs.LG cs.DScited by 4

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Abstract (arXiv)

We design improved approximation algorithms for NP-hard graph problems by incorporating predictions (e.g., learned from past data). Our prediction model builds upon and extends the varepsilon-prediction framework by Cohen-Addad, d'Orsi, Gupta, Lee, and Panigrahi (NeurIPS 2024). We consider an edge-based version of this model, where each edge provides two bits of information, corresponding to predictions about whether each of its endpoints belong to an optimal solution. Even with weak predictions where each bit is only varepsilon-correlated with the true solution, this information allows us to break approximation barriers in the standard setting. We develop algorithms with improved approximation ratios for MaxCut, Vertex Cover, Set Cover, and Maximum Independent Set problems (among others). Across these problems, our algorithms share a unifying theme, where we separately satisfy constraints related to high degree vertices (using predictions) and low-degree vertices (without using predictions) and carefully combine the answers.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

11 of 33 references.

References · 11

  1. 2018Competitive caching with machine learned advicecited by 194
  2. 2020Algorithms with Predictionscited by 152
  3. 2021Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddingscited by 48
  4. 2022Faster Fundamental Graph Algorithms via Learned Predictionscited by 42
  5. 2021Learning-Augmented k-means Clusteringcited by 26
  6. 2022Triangle and Four Cycle Counting with Predictions in Graph Streamscited by 20
  7. 2024Learning-augmented Maximum Independent Setcited by 11
  8. 2024Approximation Algorithms for Combinatorial Optimization with Predictionscited by 8
  9. 2024Parsimonious Learning-Augmented Approximations for Dense Instances of NP-hard Problemscited by 7
  10. 2023A centrality measure for quantifying spread on weighted, directed networkscited by 6
  11. 2024Constraint Satisfaction Problems with Advicecited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Learning-augmented smooth integer programs with PAC-learnable oraclescited by 1
  2. 2026Learning-Augmented Approximation for Unrelated-Machines Makespan Schedulingcited by 0
  3. 2026Learning Augmented Exact Exponential Algorithmscited by 0
  4. 2026CASP: Learning-Augmented Offline Approximation with Verifiable Certificates and Bounded-Loss PAC Guaranteescited by 0