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..
Matrix Completion Under Monotonic Single Index Models
Authors: Ravi Sastry Ganti, Laura Balzano, Rebecca Willett
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on synthetic and real-world datasets demonstrate the competitiveness of the proposed approach. |
| Researcher Affiliation | Academia | Ravi Ganti Wisconsin Institutes for Discovery UW-Madison EMAIL Laura Balzano Electrical Engineering and Computer Sciences University of Michigan Ann Arbor EMAIL Rebecca Willett Department of Electrical and Computer Engineering UW-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 Monotonic Matrix Completion (MMC) |
| Open Source Code | No | The paper does not provide an explicit statement or a link for the open-sourcing of its own methodology's code. It mentions using 'a standard off-the-shelf implementation from TFOCS [27]' for a baseline, but not for their proposed method. |
| Open Datasets | Yes | We performed experimental comparisons on four real world datasets: paper recommendation, Jester3, ML-100k, Cameraman... ML-100k comes with its own training and testing dataset. |
| Dataset Splits | Yes | For the Jester-3 dataset we used 5 randomly chosen ratings for each user for training, 5 randomly chosen rating for validation and the remaining for testing. ML-100k comes with its own training and testing dataset. We used 20% of the training data for validation. For the Cameraman and the paper recommendation datasets 20% of the data was used for training, 20% for validation and the rest for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used (e.g., GPU/CPU models, memory) to run the experiments. |
| Software Dependencies | No | The paper mentions using 'a standard off-the-shelf implementation from TFOCS [27]' but does not provide specific version numbers for any software dependencies used in their experiments. |
| Experiment Setup | Yes | For our synthetic experiments we generated a random 30 20 matrix Z of rank 5 by taking the product of two random Gaussian matrices of size n r, and r m, with n = 30, m = 20, r = 5. The matrix M was generated using the function, g (M i,j) = 1/(1 + exp( c Z i,j)), where c > 0. ... Hence, we set T = 50. |