Awesome

conf-mc (cmc)

Conformalized matrix completion

We propose a distribution-free method for predictive inference in the matrix completion problem. Our method adapts the framework of conformal prediction, which provides confidence intervals with guaranteed distribution-free validity in the setting of regression, to the problem of matrix completion. Our resulting method, conformalized matrix completion (cmc), offers provable predictive coverage regardless of the accuracy of the low-rank model. Empirical results on simulated and real data demonstrate that cmc is robust to model misspecification while matching the performance of existing model-based methods when the model is correct.

Implementation

• In the folder python, codes are available for simulations with homogeneous missingness (conf-mc-synthetic.ipynb), heterogeneous missingness (conf-mc-hetero.ipynb), and sales dataset (conf-mc-sales.ipynb).
• In the folder plot, figures in the paper can be reproduced from simulation results stored in results.