Stochastic Proximal Gradient Descent with Acceleration Techniques
Authors: Atsushi Nitanda
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Numerical Experiments In this section, we compare Acc-Prox-SVRG with Prox-SVRG and APG on L1-regularized multiclass logistic regression with the regularization parameter λ. Table 2 provides details of the datasets... Figure 2 compares Acc-Prox-SVRG with Prox-SVRG and APG. |
| Researcher Affiliation | Industry | Atsushi Nitanda NTT DATA Mathematical Systems Inc. 1F Shinanomachi Rengakan, 35, Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan nitanda@msi.co.jp |
| Pseudocode | Yes | Figure 1: Acc-Prox-SVRG |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. No repository link or explicit code release statement is found. |
| Open Datasets | Yes | These datasets can be found at the LIBSVM website1. 1http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | Table 2: Details of data sets and regularization parameters. The table provides 'Training size' and 'Testing size' for each dataset (e.g., mnist: Training size 60,000, Testing size 10,000), but does not specify a validation split or methodology for creating such splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We ran Acc Prox-SVRG using values of η from the range {0.01, 0.05, 0.1, 0.5, 1.0, 5.0, 10.0}, and we chose the best η in each mini-batch setting. For this, we set m = δb (δ {0.1, 1.0, 10}) and βk = b 2 / (b+2) varying b in the set {100, 500, 1000}. |