Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Stochastic Proximal Gradient Descent with Acceleration Techniques
Authors: Atsushi Nitanda
NeurIPS 2014 | Venue PDF | 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 EMAIL |
| 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}. |