Almost Tune-Free Variance Reduction
Authors: Bingcong Li, Lingda Wang, Georgios B. Giannakis
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical tests corroborate the proposed methods. |
| Researcher Affiliation | Academia | 1Uiversity of Minnesota, MN, USA. 2University of Illinois at Urbana-Champaign, IL, USA. |
| Pseudocode | Yes | Algorithm 1 SVRG; Algorithm 2 SARAH |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | To assess performance, the proposed tune-free BB-SVRG and BB-SARAH are applied to binary classification via regularized logistic regression (cf. (6)) using the datasets a9a, rcv1.binary, and real-sim from LIBSVM. Online available at https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/binary.html. |
| Dataset Splits | No | The paper mentions using datasets a9a, rcv1.binary, and real-sim, and states that "Details regarding the datasets, the µ values used, and implementation details are deferred to Appendix D.2." However, Appendix D.2 (and the main text) does not provide specific training/test/validation dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) 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 | For SVRG and SARAH, we fix m = 5κ, and tune for the best step sizes. For BB-SVRG, we choose ηs and ms as (9) with θκ = 4κ (as in Proposition 1) and c = 1. While we choose θκ = κ (as in Proposition 2) and c = 1 for BB-SARAH. W-Avg is adopted for both BB-SVRG and BB-SARAH. |