Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent

Chi Jin, Sham M. Kakade, Praneeth Netrapalli

Nov-21-2025, 05:37:50 GMT–Neural Information Processing Systems

Matrix completion, where we wish to recover a low rank matrix by observing a few entries from it, is a widely studied problem in both theory and practice with wide applications.

artificial intelligence, machine learning, matrix completion, (16 more...)

Neural Information Processing Systems

Nov-21-2025, 05:37:50 GMT

Conferences PDF

Country:
- Asia
  - India (0.04)
  - Middle East > Jordan (0.04)
- Europe > Spain
  - Catalonia > Barcelona Province > Barcelona (0.04)
- North America > United States
  - California > Alameda County > Berkeley (0.04)

Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning > Statistical Learning
    - Gradient Descent (0.88)
  - Representation & Reasoning > Personal Assistant Systems (0.94)

Duplicate Docs Excel Report

Title
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent

Similar Docs Excel Report more

Title	Similarity	Source
None found