Sparse Extreme Multi-label Learning with Oracle Property
Authors: Weiwei Liu, Xiaobo Shen
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments verify our theoretical findings and the superiority of our proposed estimator.Empirical results on various data sets validate our theoretical results and the superiority of our proposed estimator. |
| Researcher Affiliation | Academia | 1School of Computer Science, Wuhan University, Wuhan, China 2School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China. |
| Pseudocode | No | The paper describes the optimization algorithm using mathematical equations and prose, but does not present it in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | The experiments are conducted on a variety of real world multi-label data sets 1... 1http://manikvarma.org/downloads/XC/ XMLRepository.html |
| Dataset Splits | Yes | The split of training and testing sets in the data sets is publicly available in (Bhatia et al., 2015).The parameter λ is tuned by validation on a small validation set.the number of nearest neighbours are selected via cross validation. |
| Hardware Specification | Yes | All the computations are performed on a Red Hat Enterprise 64-Bit Linux workstation with 18-core Intel Xeon CPU E5-2680 2.80 GHz processor and 256 GB memory. |
| Software Dependencies | No | The paper mentions the operating system ("Red Hat Enterprise 64-Bit Linux") but does not provide specific version numbers for software dependencies such as libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | For the proposed methods, as µ > ζ in the estimator, µ is set to µ = 2/(b 1) for SML-SCAD and µ = 2/b for SML-MCP, respectively. In addition, b is empirically set as 3 and 2 for SML-SCAD and SML-MCP, respectively. The parameter λ is tuned by validation on a small validation set. The dimensions of embedding are set as 100 and 50 for the medium-sized and large-scale data sets, respectively. |