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-Reduced Stochastic Gradient Descent on Streaming Data
Authors: Ellango Jothimurugesan, Ashraf Tahmasbi, Phillip Gibbons, Srikanta Tirthapura
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical and experimental results show that the risk of STRSAGA is comparable to that of an offline algorithm on a variety of input arrival patterns, and its experimental performance is significantly better than prior algorithms suited for streaming data, such as SGD and SSVRG. |
| Researcher Affiliation | Academia | Ellango Jothimurugesan Carnegie Mellon University EMAIL Ashraf Tahmasbi Iowa State University EMAIL Phillip B. Gibbons Carnegie Mellon University EMAIL Srikanta Tirthapura Iowa State University EMAIL |
| Pseudocode | Yes | Algorithm 1 depicts the steps taken to process the zero or more points Xi arriving at time step i. |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the methodology described in this paper was found. |
| Open Datasets | Yes | For logistic regression, we use the A9A [DKT17] and RCV1.binary [LYRL04] datasets, and for matrix factorization, we use two datasets of user-item ratings from Movielens [HK16]. More detail on the datasets are provided in the supplementary material. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits (e.g., percentages or sample counts) in the main text. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory) used for the experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | In our experiments, the training data arrives over the course of 100 time steps, with skewed arrivals parameterized by M = 8λ. At each time step i, a streaming data algorithm has access to ρ gradient computations to update the model; we show results for ρ/λ = 1 and ρ/λ = 5. |