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..
A Note on Entrywise Consistency for Mixed-data Matrix Completion
Authors: Yunxiao Chen, Xiaoou Li
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed methods are evaluated by simulation studies and real-data applications for collaborative filtering and large-scale educational assessment. |
| Researcher Affiliation | Academia | Yunxiao Chen EMAIL Department of Statistics London School of Economics and Political Science London WC2A 2AE, UK; Xiaoou Li EMAIL School of Statistics University of Minnesota Minneapolis, MN 55455 |
| Pseudocode | Yes | Algorithm 1: Refinement Procedure without Data Splitting; Algorithm 2: Refinement Procedure with Data Splitting; Algorithm 3: Refinement Procedure with Multiple Data Splittings |
| Open Source Code | Yes | The computation code used in Sections 5 and 6 can be found at https://github.com/yunxiaochen/Matrix Completion_Mixed Data. |
| Open Datasets | Yes | We apply the proposed method to a Movie Lens dataset for movie recommendation (Harper and Konstan, 2015). We apply the proposed method to data from the 2018 Program for International Student Assessment (PISA; OECD, 2019a) |
| Dataset Splits | Yes | To evaluate the procedures, we split the data into training and test datasets, where the training and test sets contain 80% and 20% of the observed entries, respectively. Similar to the above analysis, we split 80% and 20% of the data into training and test sets and evaluate the prediction accuracy by the test-set log-likelihood. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments or simulations. |
| Software Dependencies | No | The paper states that computation code is available via a GitHub link, but it does not specify any software dependencies with version numbers in the text. |
| Experiment Setup | Yes | In the implementation, we set C2 = 2 p r/p in Algorithms 1, 2, and 3. We set ρ = r in the NBE and C = r in the CJMLE. |