Copula Multi-label Learning
Authors: Weiwei Liu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretically, we show that our estimator is an unbiased and consistent estimator and follows asymptotically a normal distribution. Moreover, we bound the mean squared error of estimator. The experimental results from various domains validate the superiority of our proposed approach. |
| Researcher Affiliation | Academia | Weiwei Liu School of Computer Science, Wuhan University Wuhan, China 430072 liuweiwei863@gmail.com |
| Pseudocode | No | The paper describes the model and estimation procedures using mathematical equations and textual descriptions, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using the code provided by the respective authors for baseline methods but does not provide a statement or link for the open-sourcing of their own methodology's code. |
| Open Datasets | Yes | This section evaluates the performance of the proposed method on five real-world benchmark data sets with various domains: EMOTIONS (music), SCENE (image), MEDICAL (text), YEAST (biology) and ENRON (text). The statistics of these data sets are presented in the website1. 1http://mulan.sourceforge.net/datasets-mlc.html |
| Dataset Splits | Yes | We perform 3-fold cross-validation on each data set and report the mean and standard error of each evaluation measurement. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | The bandwidth is set to h = 0.1 in the experiment. |