Conformalized matrix completion
Authors: Yu Gui, Rina Barber, Cong Ma
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. In this section, we conduct numerical experiments to verify the coverage guarantee of conformalized matrix completion (cmc) using both synthetic and real datasets. |
| Researcher Affiliation | Academia | Yu Gui Rina Foygel Barber Cong Ma Department of Statistics, University of Chicago {yugui,rina,congm}@uchicago.edu |
| Pseudocode | Yes | Algorithm 1 Conformalized matrix completion (cmc) |
| Open Source Code | Yes | All the results in this section can be replicated with the code available at https: //github.com/yugjerry/conf-mc. |
| Open Datasets | Yes | We also compare conformalized approaches with model-based approaches using the Rossmann sales dataset4 (Farias et al., 2022). |
| Dataset Splits | Yes | Split the data: draw Wij i.i.d. Bern(q), and define training and calibration sets Str = {(i, j) S : Wij = 1}, and Scal = {(i, j) S : Wij = 0}. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) are mentioned in the paper for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'alternating least squares (als)' and 'convex relaxation' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Throughout the experiments, we set the true rank r = 8, the desired coverage rate 1 α = 0.90, and report the average results over 100 random trials. In the synthetic setting, we generate the data matrix by M = M + E, where M is a rank r matrix, and E is a noise matrix (with distribution specified below). The low-rank component M is generated by M = κU V , where U Rd1 r is the orthonormal basis of a random d1 r matrix consisting of i.i.d. entries from a certain distribution Pu,v (specified below) and V Rd2 r is independently generated in the same manner. |