Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Almost Tune-Free Variance Reduction
Authors: Bingcong Li, Lingda Wang, Georgios B. Giannakis
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,937 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,719 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |