Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Single Pass PCA of Matrix Products
Authors: Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,074 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,432 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | in addition we also provide results from an Apache Spark implementation1 that shows better computational and statistical performance on real-world and synthetic evaluation datasets. |
| Researcher Affiliation | Academia | Shanshan Wu The University of Texas at Austin EMAIL Srinadh Bhojanapalli Toyota Technological Institute at Chicago EMAIL Sujay Sanghavi The University of Texas at Austin EMAIL Alexandros G. Dimakis The University of Texas at Austin EMAIL |
| Pseudocode | Yes | Algorithm 1 SMP-PCA: Streaming Matrix Product PCA |
| Open Source Code | Yes | The source code is available at [18].S. Wu, S. Bhojanapalli, S. Sanghavi, and A. Dimakis. Github repository for "single-pass pca of matrix products". https://github.com/wushanshan/Matrix Product PCA, 2016. |
| Open Datasets | Yes | We test our algorithm on synthetic datasets and three real datasets: SIFT10K [9], NIPS-BW [11], and URL-reputation [12]. |
| Dataset Splits | No | The paper mentions using specific datasets (SIFT10K, NIPS-BW, URL-reputation) but does not provide explicit details on how these datasets were split into training, validation, or test sets, nor does it specify proportions or sample counts for each split. |
| Hardware Specification | Yes | using a 150GB synthetic dataset on m3.2xlarge Amazon EC2 instances6. ... 6Each machine has 8 cores, 30GB memory, and 2 80GB SSD. |
| Software Dependencies | Yes | We implement our SMP-PCA in Apache Spark 1.6.2 [19]. |
| Experiment Setup | Yes | For all rest experiments, unless otherwise specified, we set r = 5, T = 10, and m as 4nr log n. |