Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Conformalized matrix completion
Authors: Yu Gui, Rina Barber, Cong Ma
NeurIPS 2023 | Venue PDF | 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 EMAIL |
| 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 = ÎșU 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. |