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..
Exponentially convergent stochastic k-PCA without variance reduction
Authors: Cheng Tang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present our empirical evaluation of Algorithm 1 to understand its convergence property on low-rank or effectively low-rank datasets 2. We first verified its performance on simulated low-rank data and effectively low-rank data, and then we evaluated its performance on two real-world effectively low-rank datasets. |
| Researcher Affiliation | Industry | Amazon AI New York, NY, 10001 EMAIL |
| Pseudocode | Yes | Algorithm 1 Matrix Krasulina |
| Open Source Code | Yes | Code will be available at https://github.com/chengtang48/neurips19. |
| Open Datasets | Yes | For MNIST [29], we use the 60000 training examples of digit pixel images, with d = 784. |
| Dataset Splits | No | The paper mentions using 60000 training examples for MNIST but does not explicitly detail any train/validation/test splits, specific percentages, or how validation was performed. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We initialized Algorithm 1 with a random matrix W o and ran it for one or a few epochs, each consists of 5000 iterations. We compare Algorithm 1 against the exponentially convergent VR-PCA: we initialize the algorithms with the same random matrix and we train (and repeated for 5 times) using the best constant learning rate we found empirically for each algorithm. |