Online Matrix Completion with Side Information
Authors: Mark Herbster, Stephen Pasteris, Lisa Tse
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 |