Robust Gradient-Based Markov Subsampling
Authors: Tieliang Gong, Quanhan Xi, Chen Xu4004-4011
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess the performance of GMS, we conduct experiments on both simulation studies and real data examples. All numerical studies are conducted in software R on Compute Canada clusters with 2.1 GHz CPUs and 128 GB memory. In simulation studies, we generate the data by y = Xβ +ε, where the n d design matrix X is generated by a mixture of Gaussian distributions. Due to space limitation, we only show the results for the setting n = 1M, d = 500. Other results are given in the supplementary material. Figs. 3 and 4 record the boxplots based on 50 times empirical estimation error. The mean and standard deviation of EE are reported in Tables 1 and 2. |
| Researcher Affiliation | Academia | Tieliang Gong, Quanhan Xi, Chen Xu Deparment of Mathematics and Statistics, University of Ottawa, Ottawa, ON, K1N6N5, Canada |
| Pseudocode | Yes | Algorithm 1 Robust Gradient-based Markov Subsampling |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of its source code. |
| Open Datasets | Yes | Online News Popularity (n = 39797, d = 61), Wave Energy Converters (n = 288000, d = 32) and Poker Hands (n = 25010, d = 11) 1. Footnote 1 refers to https://archive.ics.uci.edu/ml/datasets.php |
| Dataset Splits | No | The paper describes a subsampling strategy for estimation but does not provide explicit training, validation, and test dataset splits in the conventional sense (e.g., percentages or counts for each split). |
| Hardware Specification | Yes | All numerical studies are conducted in software R on Compute Canada clusters with 2.1 GHz CPUs and 128 GB memory. |
| Software Dependencies | No | The paper mentions 'software R' but does not specify a version number or any specific R packages with version numbers. |
| Experiment Setup | Yes | In all experiments, the subsample size is set by nsub = sr n, where sr represents the sampling ratio. We set sr = 0.001, 0.005, 0.01 for each model. If required, a pilot estimator is calculated by uniform subsampling of size n0 = nsub. |