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..
Sparse Extreme Multi-label Learning with Oracle Property
Authors: Weiwei Liu, Xiaobo Shen
ICML 2019 | Venue PDF | 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. |