Fast OSCAR and OWL Regression via Safe Screening Rules
Authors: Runxue Bao, Bin Gu, Heng Huang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a variety of datasets show that our screening rule leads to a significant computational gain without any loss of accuracy, compared to existing competitive algorithms. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA 2JD Finance America Corporation. |
| Pseudocode | Yes | Algorithm 1 Safe Screening Rule for OWL Regression with Iterative Strategy; Algorithm 2 Accelerated Proximal Gradient Descent Algorithm with Safe Screening Rules; Algorithm 3 Stochastic Proximal Gradient Descent Algorithm with Safe Screening Rules. |
| Open Source Code | No | The paper states that algorithms are implemented in MATLAB but does not provide a link or explicit statement about code availability. |
| Open Datasets | Yes | Duke Breast Cancer and Colon Cancer datasets are from the LIBSVM repository, which is available at https: //www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/. Indoor Loc Longitude and Slice Localization datasets are from the UCI benchmark repository (Dua & Graff, 2017), which is available at https://archive.ics.uci.edu/ml/datasets.php. |
| Dataset Splits | No | To compare the prediction error of different algorithms, we randomly divide the dataset into the training and testing set in proportion to 4 : 1 and use root mean squared error (RMSE) as the performance criterion of the linear regression tasks. The paper specifies a training and testing split, but no explicit validation split. |
| Hardware Specification | Yes | Our experiments were performed on a 4-core Intel i7-6820 machine. |
| Software Dependencies | No | We implement all the algorithms in MATLAB. The paper mentions MATLAB but does not provide a specific version number or other software dependencies with versions. |
| Experiment Setup | Yes | tolerance error ϵ of duality gap and dual infeasibility in our experiments are set as 10 6. ... the hyperparameters of the size of mini-batch, the number of inner loop and step size η, which range from 5 to 100, 5 to 80 and 10 6 to 10 3 respectively for different datasets, are selected by grid search. ... we set pi = i e τ, i = 1, 2, 3, τ = 2 for Duke Breast Cancer, Indoor Loc Longitude and Slice Localization datasets and τ = 3 for Colon Cancer, Cardiac Left and Cardiac Right datasets. |