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..
Online Matrix Completion with Side Information
Authors: Mark Herbster, Stephen Pasteris, Lisa Tse
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Proofs as well as an experiment on synthetic data are contained in the appendices. |
| Researcher Affiliation | Academia | Department of Computer Science University College London London WC1E 6BT, England, UK |
| Pseudocode | Yes | Algorithm 1 Predicting a binary matrix with side information in the transductive setting. and Algorithm 2 Predicting a binary matrix with side information in the inductive setting. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions 'an experiment on synthetic data' but does not provide a link, DOI, or specific citation to a publicly available dataset used for training or experimentation. |
| Dataset Splits | No | The paper mentions conducting an experiment on 'synthetic data' but does not specify any training, validation, or test dataset splits, nor does it refer to predefined splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) used for the experiments. |
| Experiment Setup | Yes | Parameters: Learning rate: 0 < , quasi-dimension estimate: 1 b D, margin estimate: 0 < γ 1, non-conservative flag [NON-CONSERVATIVE] 2 {0, 1} and side information matrices M 2 Sm++ with m + n 3 |