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..
Convex Co-embedding
Authors: Farzaneh Mirzazadeh, Yuhong Guo, Dale Schuurmans
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation reveals the advantages of global training in different case studies. |
| Researcher Affiliation | Academia | Farzaneh Mirzazadeh Department of Computing Science University of Alberta Edmonton, AB T6G 2E8, Canada EMAIL; Yuhong Guo Department of Computer and Information Sciences, Temple University Philadelphia, PA 19122, USA EMAIL; Dale Schuurmans Department of Computing Science University of Alberta Edmonton, AB T6G 2E8, Canada EMAIL |
| Pseudocode | No | The paper describes algorithms and methods but does not include a clearly labeled pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The paper uses the multi-label data sets Corel5K, Emotion, Mediamill, Scene, and Yeast (Table 1), which are well-known benchmark datasets. For the tag recommendation, it uses 'Bib Sonomy' data, following '(J aschke et al. 2008) we exploit the core at level 10 subsample'. |
| Dataset Splits | No | The paper states: 'we used 1000 examples for training and the rest for testing (except Emotion where we used a 2/3 train-test split), repeating 10 times for different random splits.' It does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific details about ancillary software, such as library names with version numbers. |
| Experiment Setup | Yes | The paper specifies hyperparameters such as regularization parameters λ (e.g., 'a common regularization parameter λ = λ1 = λ2 to the trace and squared Frobenius norm regularizers'), mentions specific loss functions ('smoothed version (28) of the large margin multi-label loss (27)', 'ranking logistic loss function'), and discusses initialization strategies ('random initialization', 'initializing with all 0s', 'initializing from all 1s'). Specific λ values are provided in Table 2 and Table 3. |