Online Missing Value Imputation and Change Point Detection with the Gaussian Copula
Authors: Yuxuan Zhao, Eric Landgrebe, Eliot Shekhtman, Madeleine Udell9199-9207
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real world data validate the performance of the proposed methods. |
| Researcher Affiliation | Academia | Cornell University, Ithaca, NY 14853, USA {yz2295, ecl93,ess239,udell}@cornell.edu |
| Pseudocode | Yes | Algorithm 1: Online Imputation with the Gaussian Copula |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | We evaluate on a subset of the Movie Lens 1M dataset (Harper and Konstan 2015) that consists of all movies with more than 1000 ratings, with 1-5 ordinal ratings of size 6939 207 with over 75% entries missing. |
| Dataset Splits | No | The paper discusses 'training' and 'testing' in the context of its algorithms and experiments, but it does not provide explicit percentages, sample counts, or citations for train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software, as it does not include specific version numbers for key software components or libraries. |
| Experiment Setup | Yes | We find using γi = c/(i + c) with c = 5 for the offline setting and γi = 0.5 for the online setting gives good results throughout our experiments. We use 1 core for all methods. |