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..
Teaching-to-Learn and Learning-to-Teach for Multi-label Propagation
Authors: Chen Gong, Dacheng Tao, Jie Yang, Wei Liu
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough empirical studies show that due to the optimized propagation sequence designed by the teachers, ML-TLLT yields generally better performance than seven state-of-the-art methods on the typical multi-label benchmark datasets. This section first validates several critical steps in the proposed ML-TLLT, and then compares ML-TLLT with seven state-of-the-art methods on five benchmark datasets. |
| Researcher Affiliation | Collaboration | Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney Didi Research, Beijing, China |
| Pseudocode | Yes | Algorithm 1 The curvilinear search for minimizing (7) and Algorithm 2 PALM for solving S(r)-subproblem (6) |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | All the adopted datasets come from the MULAN repository. 1http://mulan.sourceforge.net/datasets-mlc.html |
| Dataset Splits | Yes | The reported results of various algorithms on all the datasets are produced by 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | In ML-TLLT, the trade-off parameters β0 and β1 are set to 1 for all the experiments. As suggested by (Chen et al. 2008), we set u = 1, v = 0.15 in SMSE-HF, and β = γ = 1 in SMSE-LGC. The weighting parameter C in MLSVM (Linear) and MLSVM (RBF) is tuned to 1. |