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..
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
Authors: Chaobing Song, Yong Jiang, Yi Ma
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on real datasets, we show the good performance of VRADA over existing methods for large-scale machine learning problems. |
| Researcher Affiliation | Academia | Tsinghua-Berkeley Shenzhen Institute, Tsinghua University EMAIL, EMAIL Department of EECS, University of California, Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1 Variance Reduction via Accelerated Dual Averaging (VRADA) |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology described in this paper was found. |
| Open Datasets | Yes | The datasets we use are a9a and covtype, downloaded from the Lib SVM website10. The dataset url is https://www.csie.ntu.edu.cn/~cjlin/libsvmtools/datasets/. |
| Dataset Splits | No | No explicit information regarding training/validation/test dataset splits (e.g., percentages, sample counts, or specific cross-validation setup) was found. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications, or cloud instances) used for running the experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names like PyTorch 1.9 or specific solver versions) were provided in the paper. |
| Experiment Setup | Yes | The problem we study is the 2-norm regularized logistic regression problem with regularization parameter λ ∈ {0, 10−8, 10−4}. All four algorithms we compare have a similar outer-inner structure, where we set all the number of iterations as m = 2n. |