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..
Generalization-Aware Structured Regression towards Balancing Bias and Variance
Authors: Martin Pavlovski, Fang Zhou, Nino Arsov, Ljupco Kocarev, Zoran Obradovic
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on both synthetic and real-world data indicate that such an objective enhances the overall model s predictive performance. |
| Researcher Affiliation | Academia | 1 Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA, USA 2 Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia |
| Pseudocode | Yes | Algorithm 1 GLACER |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | Sacramento Real-Estate. A collection of 985 real estate transactions were observed in the Greater Sacramento area, California, made over a period of one week in May 2008. ... pre-processed by [Hallac et al., 2015]. |
| Dataset Splits | Yes | Using the aforedescribed data generation procedure, 10 different training and independent test sets were generated. The Sacramento Real-Estate dataset was split into a training set of 785 house transactions and a test set of 200 transactions (same as in [Hallac et al., 2015]). As for Medicare Readmissions, half of the data was randomly sampled and used for training, while the other half was used for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | GLACER was then run on each train/test pair with M = 5, 10, 30 components, while for each value of M the subsampling fraction η varied within {0.3, 0.5, 0.7}. ... in the following experiments we chose to run GLACER with M = 10 and set η to 0.3 for efficiency. |