Hierarchical Methods of Moments

Matteo Ruffini, Guillaume Rabusseau, Borja Balle

Nov-21-2025, 12:47:04 GMT–Neural Information Processing Systems

Despite their theoretical appeal, the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods of moments to circumvent such limitations.

algorithm, artificial intelligence, machine learning, (14 more...)

Neural Information Processing Systems

Nov-21-2025, 12:47:04 GMT

Conferences PDF

Add feedback

Country:
- North America
  - United States > California
    - Los Angeles County > Long Beach (0.04)
    - Alameda County > Berkeley (0.04)
  - Canada > Quebec
    - Montreal (0.04)
- Asia > Middle East
  - Jordan (0.04)
- Africa > Senegal
  - Kolda Region > Kolda (0.04)

Technology:
- Information Technology
  - Data Science (0.93)
  - Artificial Intelligence
    - Representation & Reasoning (1.00)
    - Machine Learning
      - Learning Graphical Models (0.72)
      - Statistical Learning > Clustering (0.46)

Duplicate Docs Excel Report

Title
Hierarchical Methods of Moments
Hierarchical Methods of Moments

Similar Docs Excel Report more

Title	Similarity	Source
None found