The Potts-Ising model for discrete multivariate data
Authors: Zahra Razaee, Arash Amini
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the ability of the model to capture multivariate dependencies in real data by comparing with existing approaches. |
| Researcher Affiliation | Collaboration | Zahra S. Razaee Biostatistics and Bioinformatics Research Center, Cedars Sinai zahra.razaee@cshs.org Arash A. Amini Department of Statistics University of California, Los Angeles aaamini@ucla.edu |
| Pseudocode | No | The paper describes the model fitting process using mathematical equations and prose, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for these simulations is available at Git Hub repository aaamini/pois_comparisons. |
| Open Datasets | Yes | We consider the following publicly available datasets: Movie Lens 100K Dataset [23] of movie ratings (n = 1037, sparsity 97.8%), and an Amazon customer ratings dataset [24] (n = 745, sparsity 94.3%). |
| Dataset Splits | Yes | In the simulations, we split the data into a training and a test set. (...) The three methods POIS, T-PGM, and MIXPOI have hyper-parameters that we tuned by splitting the training set further into training and validation sets (at 70/30 ratio) and evaluating the performance on the validation set using the pair-complement MMD. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as CPU or GPU models, or cloud computing instance types used for running the experiments. |
| Software Dependencies | Yes | Firth s approach is equivalent to putting a Jeffrey s prior on γi and we use the implementation available in logistf R package [16]. (...) we take advantage of the efficiency of glmnet in using warm starts to compute the entire regularization path as of function of λ. We can then use cross-validation to tune λ as will be discussed in Section 4. [16] G. Heinze et al. R Package logistf . http://CRAN.R-project.org/package=logistf. 2018. [17] J. Friedman et al. R Package glmnet . http://CRAN.R-project.org/package=glmnet. 2018. |
| Experiment Setup | Yes | For the POIS model, we used a single regularization parameter λ for the ℓ1 penalty in all the neighborhood regressions. We used 15 values of λ between 10 4 to 10 1.3, equally spaced on the log-scale. (...) The sampling parameter is generally taken to be m = 1000 in our simulations. (...) The results are further averaged over n CV = 5 random training-test splits. |