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..
StopWasting My Gradients: Practical SVRG
Authors: Reza Babanezhad Harikandeh, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konečný, Scott Sallinen
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present experimental results that evaluate our proposed variations on the SVRG method. |
| Researcher Affiliation | Academia | Department of Computer Science University of British Columbia, School of Mathematics University of Edinburgh, Department of Electrical and Computer Engineering University of British Columbia |
| Pseudocode | Yes | Algorithm 1 Batching SVRG, Algorithm 2 Mixed SVRG and SG Method, Algorithm 3 Heuristic for skipping evaluations of fi at x |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | We consider the datasets used by [1], whose properties are listed in the supplementary material. |
| Dataset Splits | No | The paper refers to datasets used by [1] and their properties in the supplementary material but does not explicitly provide specific training, validation, and test split information within the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | As in their work we add a bias variable, normalize dense features, and set the regularization parameter λ to 1/n. We used a step-size of α = 1/L and we used m = |Bs| which gave good performance across methods and datasets. |