Exact Recovery of Hard Thresholding Pursuit
Authors: Xiaotong Yuan, Ping Li, Tong Zhang
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results on simulated data confirms our theoretical predictions. In this section, we conduct a group of Monte-Carlo simulation experiments on sparse linear regression and sparse logistic regression models to verify our theoretical predictions. |
| Researcher Affiliation | Academia | Xiao-Tong Yuan B-DAT Lab Nanjing University of Info. Sci.&Tech. Nanjing, Jiangsu, 210044, China xtyuan@nuist.edu.cn Ping Li Tong Zhang Depart. of Statistics and Depart. of CS Rutgers University Piscataway, NJ, 08854, USA {pingli,tzhang}@stat.rutgers.edu |
| Pseudocode | Yes | Algorithm 1: Hard Thresholding Pursuit. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that its source code is publicly available. |
| Open Datasets | No | We consider a synthetic data model in which the sparse parameter w is a p = 500 dimensional vector that has k = 50 nonzero entries drawn independently from the standard Gaussian distribution. |
| Dataset Splits | No | The paper describes generating synthetic data and varying sample size 'n', but does not explicitly state train/validation/test dataset splits or their proportions. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies (e.g., library names with version numbers) used for the experiments. |
| Experiment Setup | Yes | Data generation: We consider a synthetic data model in which the sparse parameter w is a p = 500 dimensional vector that has k = 50 nonzero entries drawn independently from the standard Gaussian distribution. Each data sample u is a normally distributed dense vector. For the linear regression model, the responses are generated by v = u w + ε where ε is a standard Gaussion noise. For the logistic regression model, the data labels, v { 1, 1}, are then generated randomly according to the Bernoulli distribution P(v = 1|u; w) = exp(2 w u)/(1 + exp(2 w u)). We allow the sample size n to be varying and for each n, we generate 100 random copies of data independently. In our experiment, we test HTP with varying sparsity level k k. |