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..
Uncorrelated Group LASSO
Authors: Deguang Kong, Ji Liu, Bo Liu, Xuan Bao
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on real world datasets demonstrate the effectiveness of the proposed new regularization and algorithm. and To validate the effectiveness of our method, we conduct experiments on multi-label datasets for image annotation. |
| Researcher Affiliation | Collaboration | Deguang Kong1, Ji Liu2, Bo Liu3 and Xuan Bao4 1Samsung Research America, 2University of Rochester, 3Philips Research North America, 4Google Inc. |
| Pseudocode | No | The paper describes the optimization algorithm through text and mathematical equations (Eq. 11, 12) and iterative steps, but does not present it as a clearly labeled pseudocode or algorithm block/figure. |
| Open Source Code | No | The paper does not provide any specific repository links, explicit code release statements, or mention code in supplementary materials for the described methodology. |
| Open Datasets | Yes | Take the House dataset1 (n=506, p=14) as an example. ... 1http://archive.ics.uci.edu/ml/datasets/housing and Barcelona5 dataset contains... 5http://mlg.ucd.ie/content/view/61 and MSRC6 dataset contains... 6http://research.microsoft.com/en-us/projects/\ objectclassrecognition/ and TREVID20057 dataset contains... 7http://www-nlpir.nist.gov/projects/tv2005/ |
| Dataset Splits | Yes | In all the experiments, we use 5-fold cross validation, where 4-fold data are used for training and the remaining ones are used for testing purpose. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions various software concepts and methods, but does not provide specific version numbers for any ancillary software or libraries used in its experiments. |
| Experiment Setup | Yes | In our approach of Eq.(2), we use logistic loss function, group Gg is generated according to the feature correlation defined in Eq.(9), where θ = 0.3. and We adjust the parameter α such that the number of nonzero rows in W (i.e., optimal solution) is r. |