Partial Matrix Completion
This work addresses the issue of varying completion accuracy in matrix completion for applications requiring reliable predictions, but it appears incremental as it builds on existing matrix completion methods.
The paper tackles the problem of matrix completion by introducing a partial completion framework that identifies a large subset of entries that can be completed with high confidence, proposing an efficient algorithm with provable guarantees for accuracy and coverage, and including preliminary empirical evaluations.
The matrix completion problem aims to reconstruct a low-rank matrix based on a revealed set of possibly noisy entries. Prior works consider completing the entire matrix with generalization error guarantees. However, the completion accuracy can be drastically different over different entries. This work establishes a new framework of partial matrix completion, where the goal is to identify a large subset of the entries that can be completed with high confidence. We propose an efficient algorithm with the following provable guarantees. Given access to samples from an unknown and arbitrary distribution, it guarantees: (a) high accuracy over completed entries, and (b) high coverage of the underlying distribution. We also consider an online learning variant of this problem, where we propose a low-regret algorithm based on iterative gradient updates. Preliminary empirical evaluations are included.