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..
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Authors: Mao Ye, Yan Sun
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on simulated and real world datasets show the efficiency of our method in terms of variable selection and prediction accuracy. |
| Researcher Affiliation | Academia | 1Department of Statistics, Purdue University, West Lafayette, IN, USA. Correspondence to: Mao Ye <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Training Penalized Neural Network; Algorithm 2 Greedy Elimination Method |
| Open Source Code | No | The paper mentions 'Details on implementation for all experiments are in the supplementary material.' but does not explicitly state that source code for their methodology is provided or offer a specific link. |
| Open Datasets | Yes | CCLE is taken from (Liang et al., 2017) and the other 3 datasets are from UCI machine learning repository. |
| Dataset Splits | Yes | Each dataset consists of 600 observations, with 200 for training, 100 for validation and 300 for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'tanh as the activation function' and refers to algorithms like 'GIST algorithm' and 'block-wise descent algorithm', but it does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The network structure of Spinn and GEPNN are set to have 6 hidden units. We set the number of hidden units of Spinn and GEPNN to be 3 for CCLE, CCPP and Airfoil. Since we add nonlinear features for Boston Housing dataset, we reduce the number of hidden units to 2 for Spinn and GEPNN. We need to tune λ0, α, λ1 and thre(t) for the algorithm. |