Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
Authors: Zheng Qu, Peter Richtarik, Tong Zhang
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we demonstrate how Quartz specialized to different samplings compares with other methods. All of our experiments are performed with m = 1, for smoothed hinge-loss functions {φi} with γ = 1 and squared L2-regularizer g, see [20]. The experiments were performed on the three datasets reported in Table 3, and three randomly generated large dataset [12] with n = 100, 000 examples, d = 100, 000 features with different sparsity. |
| Researcher Affiliation | Academia | Zheng Qu Department of Mathematics The University of Hong Kong Hong Kong zhengqu@maths.hku.hk Peter Richtarik School of Mathematics The University of Edinburgh EH9 3FD, United Kingdom peter.richtarik@ed.ac.uk Tong Zhang Department of Statistics Rutgers University Piscataway, NJ, 08854 tzhang@stat.rutgers.edu |
| Pseudocode | Yes | Algorithm 1 Quartz |
| Open Source Code | No | The paper does not contain any explicit statement that the source code for the methodology described is publicly available, nor does it provide any links to a code repository. |
| Open Datasets | Yes | The experiments were performed on the three datasets reported in Table 3, and three randomly generated large dataset [12] with n = 100, 000 examples, d = 100, 000 features with different sparsity. Table 3 lists 'cov1', 'w8a', 'ijcnn1'. |
| Dataset Splits | No | The paper mentions performing experiments on datasets (Table 3) but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or explicit standard split references). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., specific Python, PyTorch, or library versions) that would be needed to reproduce the experiments. |
| Experiment Setup | Yes | All of our experiments are performed with m = 1, for smoothed hinge-loss functions {φi} with γ = 1 and squared L2-regularizer g, see [20]. (λ values are also given under each figure: e.g., 'λ = 1e-06') |