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 [1].
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection
Authors: Jie Wang, Jieping Ye
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real data sets demonstrate that the speedup gained by MLFre can be orders of magnitude. |
| Researcher Affiliation | Academia | Jie Wang1, Jieping Ye1,2 1Computational Medicine and Bioinformatics 2Department of Electrical Engineering and Computer Science University of Michigan, Ann Arbor, MI 48109 EMAIL |
| Pseudocode | Yes | Algorithm 1 Hierarchical Projection: PA0 1( ). |
| Open Source Code | No | The paper mentions using 'SLEP package [15]' but does not explicitly state that the code for their proposed MLFre method is open-source or provide a link to it. |
| Open Datasets | Yes | We perform experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) data set (http://adni.loni.usc.edu/). |
| Dataset Splits | No | The paper describes the construction of synthetic data and parameter tuning but does not explicitly provide specific percentages or counts for training, validation, and test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using 'SLEP package [15]' but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | For each data set, we run the solver combined with MLFre along a sequence of 100 parameter values equally spaced on the logarithmic scale of λ/λmax from 1.0 to 0.05. The data set consists of 747 patients with 406262 single nucleotide polymorphisms (SNPs). |