Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning
Authors: Lingxiao Wang, Quanquan Gu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present experimental results of our proposed algorithm on both synthetic and real datasets. We compare our algorithm with Two stage [Kifer et al., 2012] and Frank-Wolfe [Talwar et al., 2015] methods. |
| Researcher Affiliation | Academia | Lingxiao Wang and Quanquan Gu Department of Computer Science, University of California, Los Angeles {lingxw,qgu}@cs.ucla.edu |
| Pseudocode | Yes | Algorithm 1 Differentially Private Iterative Gradient Hard Thresholding (DP-IGHT) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In this experiment, we use two real datasets, E2006-TFIDF dataset [Kogan et al., 2009] and RCV1 dataset [Lewis et al., 2004], for the evaluation of sparse linear regression and sparse logistic regression, respectively. |
| Dataset Splits | Yes | E2006-TFIDF dataset, which consists of 16087 training examples, 3308 testing examples... |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | For all the experiments, we choose the variance of the random noise of different methods as suggested by their theoretical guarantees, and select other parameters, such as the step size, iteration number, and thresholding parameter by five-fold cross-validation. |