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..
Fast Sparse Group Lasso
Authors: Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our algorithm enhances the efficiency of the original algorithm without any loss of accuracy. |
| Researcher Affiliation | Collaboration | Yasutoshi Ida1,3 Yasuhiro Fujiwara2 Hisashi Kashima3,4 1NTT Software Innovation Center 2NTT Communication Science Laboratories 3Kyoto University 4RIKEN AIP |
| Pseudocode | Yes | Algorithm 1 Fast Sparse Group Lasso |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code available or provide a link to a code repository. |
| Open Datasets | Yes | We evaluated the processing time and prediction error of our approach by conducting experiments on six datasets from the LIBSVM website (abalone, cpusmall, boston, bodyfat, eunite2001, and pyrim). |
| Dataset Splits | Yes | We split the data into training and test data for each dataset. That is, 50% of a dataset was used as test data for evaluating the prediction error in terms of the squared loss for the response. |
| Hardware Specification | Yes | All the experiments were conducted on a Linux 2.20 GHz Intel Xeon server with 264 GB of main memory. |
| Software Dependencies | No | The paper mentions running experiments on 'Linux' but does not provide specific version numbers for any software, libraries, or frameworks used (e.g., Python, PyTorch, scikit-learn, etc.). |
| Experiment Setup | Yes | We tuned λ for all approaches based on the sequential rule by following the methods in [18, 12 14]. The search space was a non-increasing sequence of Q parameters (λq)Q 1 q=0 defined as λq = λmax10 δq/Q 1. We used δ = 4 and Q = 100 [18, 12 14]. For another tuning parameter α, we used the settings α [0.2, 0.4, 0.6, 0.8]. We stopped the algorithm for each λq when the relative tolerance ||β βnew||2/||βnew||2 dropped below 10 5 for all approaches [9, 10]. |