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..
Optimal Query Complexities for Dynamic Trace Estimation
Authors: David Woodruff, Fred Zhang, Richard Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our algorithmic results. We compare Algorithm 1, with the following procedures on both synthetic and real datasets. |
| Researcher Affiliation | Collaboration | David P. Woodruff Carnegie Mellon University EMAIL Fred Zhang UC Berkeley EMAIL Qiuyi (Richard) Zhang Google Brain EMAIL |
| Pseudocode | Yes | Algorithm 1: Improved Dynamic Trace Estimation; Algorithm 2: SUMTREE: Helper Function for Tracing the Binary Tree |
| Open Source Code | No | The paper states that code is included in the supplemental material or as a URL for reproducing experimental results, but does not provide a direct link or explicit statement for the methodology described within the main text. |
| Open Datasets | Yes | We use two ar Xiv collaboration networks with 5, 242 and 9, 877 nodes [17]. ... Both are available at https://sparse.tamu.edu/SNAP. ... We train the network on the MNIST dataset via mini-batch SGD |
| Dataset Splits | No | The paper mentions using specific datasets (SNAP, MNIST) and training, but does not provide explicit details on training, validation, or test data splits. |
| Hardware Specification | No | The paper states that the total amount of compute and type of resources used are included, but the main text does not specify exact hardware details such as GPU/CPU models or processor types. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | No | The paper states that training details and hyperparameters are specified, but these details are not found in the main text of the paper, possibly in an appendix or supplementary material. |