Generalization-Aware Structured Regression towards Balancing Bias and Variance
Authors: Martin Pavlovski, Fang Zhou, Nino Arsov, Ljupco Kocarev, Zoran Obradovic
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |