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..
Self-paced Mixture of Regressions
Authors: Longfei Han, Dingwen Zhang, Dong Huang, Xiaojun Chang, Jun Ren, Senlin Luo, Junwei Han
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of SPMo R, we conducted experiments on both the sythetic examples and real-world applications to age estimation and glucose estimation. The results show that SPMo R outperforms the stateof-the-arts methods. |
| Researcher Affiliation | Academia | 1School of Information and Electronics, Beijing Institute of Technology 2School of Automation, Northwestern Polytechnical University 3School of Computer Science, Carnegie Mellon University 4Beijing Electro-Mechanical Engineering Institute |
| Pseudocode | Yes | Algorithm 1 SPMo R Training Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Dataset: We conducted experiments on the most frequently used Longitudinal Morphological Face Database (MORPH) [Ricanek and Tesafaye, 2006] database... and Dataset: We conducted experiments on the popular 2009-2014 National Health and Nutrition Examination Survey (NHANES) dataset [Zipf et al., 2013], which is the cross-sectional data and the ground-truth Hb A1c data were publicly available. |
| Dataset Splits | Yes | randomly dividing the whole dataset into two parts: 80% for training and the other 20% for test, and repeating 30 random trails. Next, in the first run, the optimal hyper-parameters, including k and λ, were obtained by using grid-search with tenfold CV on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions features and analysis methods, but does not provide specific software names with version numbers (e.g., Python 3.x, PyTorch 1.x) for replication. |
| Experiment Setup | Yes | In SPMo R, we set k to 9, and λ to 1e-05. In SPMo R+, we set k to 8, and λ to 1e-05. In SPMo R, we set k to 5, and λ to 1e-05. In SPMo R+, we set k to 16, and λ to 1e-04. |