mlsanityData to 14 Oct 2026

Density Level Sets: Asymptotics, Inference, and Visualization

arXiv 1504.05438 ↗First posted 21 Apr 2015stat.ME math.ST stat.THcited by 11

Not on the map. Why?

Abstract (arXiv)

We derive asymptotic theory for the plug-in estimate for density level sets under Hausdoff loss. Based on the asymptotic theory, we propose two bootstrap confidence regions for level sets. The confidence regions can be used to perform tests for anomaly detection and clustering. We also introduce a technique to visualize high dimensional density level sets by combining mode clustering and multidimensional scaling.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Apr 2015.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 2; 2025: 1.

11 papers.

  1. 2023Non-Parametric Outlier Synthesiscited by 129
  2. 2024Learning with Mixture of Prototypes for Out-of-Distribution Detectioncited by 43
  3. 2024Conformal confidence sets for biomedical image segmentationcited by 6
  4. 2021Boundary Estimation from Point Clouds: Algorithms, Guarantees and Applicationscited by 4
  5. 2021Moving Up the Cluster Tree with the Gradient Flowcited by 3
  6. 2021Optimal subgroup selectioncited by 2
  7. 2022Clustering by Hill-Climbing: Consistency Resultscited by 2
  8. 2021An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Treecited by 1
  9. 2025Balanced Hyperbolic Embeddings Are Natural Out-of-Distribution Detectorscited by 1
  10. 2022Bump hunting through density curvature featurescited by 0
  11. 2022Learning Acceptance Regions for Many Classes with Anomaly Detectioncited by 0