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.
Sketching Algorithms and Lower Bounds for Ridge Regression
Authors: Praneeth Kacham, David Woodruff
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 26,983 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,580 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 | We run our algorithm on a ridge regression instance with a 6000 70000 matrix A whose entries are independent Gaussian random variables. We set λ such that σ2/λ 1. Na ıvely computing x = AT(AAT + λI) 1b takes tnaive = 71 seconds on our machine. We use OSNAP with sparsity s = 8 and vary the number r of rows and observe the running times and quality of the solution that is obtained by our algorithm.Our experiments show the general trends we expect. Increasing the number of rows in the sketching matrix results in a solution x that has lower cost and also is closer to the optimum solution x . |
| Researcher Affiliation | Academia | 1Computer Science Department, Carnegie Mellon University. Correspondence to: Praneeth Kacham <EMAIL>, David P. Woodruff <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 RIDGEREGRESSION |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the public release of the source code for the described methodology. |
| Open Datasets | No | We run our algorithm on a ridge regression instance with a 6000 70000 matrix A whose entries are independent Gaussian random variables. |
| Dataset Splits | No | The paper mentions running an experiment on a generated matrix A but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions 'Na ıvely computing x = AT(AAT + λI) 1b takes tnaive = 71 seconds on our machine.' but does not provide specific hardware details for 'our machine'. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We run our algorithm on a ridge regression instance with a 6000 70000 matrix A whose entries are independent Gaussian random variables. We set λ such that σ2/λ 1. Na ıvely computing x = AT(AAT + λI) 1b takes tnaive = 71 seconds on our machine. We use OSNAP with sparsity s = 8 and vary the number r of rows and observe the running times and quality of the solution that is obtained by our algorithm. |