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..
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). |