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..
Regularized Modal Regression with Applications in Cognitive Impairment Prediction
Authors: Xiaoqian Wang, Hong Chen, Weidong Cai, Dinggang Shen, Heng Huang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the application side, we applied our model to successfully improve the cognitive impairment prediction using the Alzheimer s Disease Neuroimaging Initiative (ADNI) cohort data. |
| Researcher Affiliation | Academia | 1 Department of Electrical and Computer Engineering, University of Pittsburgh, USA 2School of Information Technologies, University of Sydney, Australia 3 Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA EMAIL,EMAIL EMAIL,EMAIL,EMAIL |
| Pseudocode | No | The paper describes the optimization algorithm in text and equations, but does not provide a formally labeled pseudocode block or algorithm steps in a structured format. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Here we present the comparison results on six benchmark datasets from UCI repository [15] and Stat Lib2, which include: slumptest, forest๏ฌre, bolts, cloud, kidney, and lupus. ... [15] M. Lichman. UCI machine learning repository, 2013. 2http://lib.stat.cmu.edu/datasets/. Data used in this article were obtained from the ADNI database (adni. loni.usc.edu). |
| Dataset Splits | Yes | For evaluation, we calculate root mean square error (RMSE) between the predicted value and ground truth in out-of-sample prediction. We employ 2-fold cross validation and report the average performance for each method. For each method, we set the hyper-parameter of the regularization term in the range of {10 4, 10 3.5, . . . , 104}. We tune the hyper-parameters via 2-fold cross validation on the training data and report the best parameter w.r.t. RMSE of each method. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify versions for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | For each method, we set the hyper-parameter of the regularization term in the range of {10 4, 10 3.5, . . . , 104}. We tune the hyper-parameters via 2-fold cross validation on the training data and report the best parameter w.r.t. RMSE of each method. For RMR methods, we adopt the Epanechnikov kernel and set the bandwidth as ฯ = max(|y w T x|). |