Spike and slab variational Bayes for high dimensional logistic regression
Authors: Kolyan Ray, Botond Szabo, Gabriel Clara
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We confirm the improved performance of our VB algorithm over common sparse VB approaches in a numerical study. We verify in a numerical study that the empirical performance of the proposed method reflects these theoretical guarantees. |
| Researcher Affiliation | Academia | Kolyan Ray Department of Mathematics Imperial College London kolyan.ray@ic.ac.uk Botond Szabó Department of Mathematics Vrije Universiteit Amsterdam b.t.szabo@vu.nl Gabriel Clara Department of Mathematics Vrije Universiteit Amsterdam g.clara@student.vu.nl |
| Pseudocode | Yes | Algorithm 1: Modified CAVI for variational Bayes with Laplace slabs |
| Open Source Code | Yes | We are currently working on a more efficient implementation as an R-package sparsevb [15] that should improve the run-time. [15] CLARA, G., SZABO, B., AND RAY, K. sparsevb: spike and slab variational Bayes for linear and logistic regression, 2020. R package version 1.0. |
| Open Datasets | No | The paper describes synthetic data generation, e.g., "We take n = 250, p = 500 and X to be a standard Gaussian design matrix, i.e. Xij iid N(0, 1), and set the true signal θ0 = (2, 2, 0, . . . , 0)T to be s = 2 sparse." It does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions "n training examples" but does not specify any training, validation, or test dataset splits (e.g., percentages, counts, or a specific splitting methodology) needed for reproduction. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments were found in the paper. |
| Software Dependencies | No | The paper mentions "C++ using the Rcpp interface and used the Armadillo linear algebra library and ensmallen optimization library" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We take n = 250, p = 500 and X to be a standard Gaussian design matrix, i.e. Xij iid N(0, 1), and set the true signal θ0 = (2, 2, 0, . . . , 0)T to be s = 2 sparse. We ran the experiment 200 times for each method and report the means and standard deviations of the following performance measures:... |