Streaming PCA for Markovian Data
Authors: Syamantak Kumar, Purnamrita Sarkar
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 syamantak@utexas.edu, purna.sarkar@austin.utexas.edu |
| 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. |