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..
Streaming PCA for Markovian Data
Authors: Syamantak Kumar, Purnamrita Sarkar
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, the paper presents 'Experimental Validation' with figures comparing Oja's algorithm performance. |
| Researcher Affiliation | Academia | Syamantak Kumar1 Purnamrita Sarkar2 1Department of Computer Science, UT Austin 2Department of Statistics and Data Sciences, UT Austin EMAIL, EMAIL |
| Pseudocode | No | The paper provides the update rule for Oja's algorithm as an equation (Eq. 1) but does not include a structured pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any statement about making its source code available or include a link to a code repository. |
| Open Datasets | No | The paper uses synthetic data generated internally: 'Each state s Ωis associated with D(s) := Bernoulli(ps) distribution. We set d = 1000 and select ps U (0, 0.05) at the start of each random run.' It does not refer to a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes generating data for experiments but does not specify any training, validation, or test dataset splits. The data is generated 'on the fly' for each random run. |
| Hardware Specification | No | The paper describes the experimental setup but does not provide any specific details about the hardware (e.g., GPU, CPU models) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks) used for the experiments. |
| Experiment Setup | Yes | The step sizes for Oja s algorithm are set as ηi = α (β+i)(λ1 λ2) for α = 5, β = 5 1 |λ2(P )|. For the downsampled variant, every 10th data-point is considered, and β is accordingly divided by 10. |