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..
Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning
Authors: Xiao Zhang, Wenzhong Li, Vu Nguyen, Fuzhen Zhuang, Hui Xiong, Sanglu Lu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the model performance on three public multi-task multi-label data sets, and the results show that LABTAG outperforms the compared baselines with a significant margin. 4 Experimental Evaluation |
| Researcher Affiliation | Academia | 1 State Key Laboratory for Novel Software Technology, Nanjing University, China 2 Center for Pattern Recognition and Data Analytics, Deakin University, Australia 3 Key Lab of IIP of CAS, Institute of Computing Technology, CAS Beijing, China 4 Management Science & Information Systems, Rutgers University, USA |
| Pseudocode | Yes | Algorithm 1 The generative process of the proposed LABTAG model |
| Open Source Code | No | The paper mentions utilizing and downloading code for baseline models ('MEKA toolbox', 'Matlab code from the author s website for BNMC and ML-KNN'), but does not provide concrete access to the source code for the proposed LABTAG model. |
| Open Datasets | Yes | We use three public data sets for the performance evaluation, including Trip Advisor, LDOS-Co Mo Da and Enron Corpus data sets... Trip Advisor and LDOS-Co Mo Da are two context-aware data sets, which are used for context recommendation systems [Zheng et al., 2014]... Finally, the Enron Email Corpus contains email information (email content and recipients) from Enron [Klimt and Yang, 2004; Carvalho and Cohen, 2007]. |
| Dataset Splits | Yes | In each task, 50% data are used for training and the remaining 50% for test. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'MEKA toolbox' and 'Matlab code' used for baselines, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The hyper-parameter settings of LABTAG model are as follows: α = 1, δ = 0.01, ϖ = 0.07, β = 1, µ0 = 0, Σ0 = 10I. The truncation threshold is set as 0.001 #Train and the learning rate is set as 0.01. |