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..
An online passive-aggressive algorithm for difference-of-squares classification
Authors: Lawrence Saul
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental results We experimented on the INFIMNIST data set of handwritten digit images [7], a purposefully constructed superset of the original MNIST data set [73]. |
| Researcher Affiliation | Academia | Lawrence K. Saul Department of Computer Science and Engineering University of California, San Diego 9500 Gilman Drive, Mail Code 0404 La Jolla, CA 92093-0404 EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical equations and text, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not state that source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We experimented on the INFIMNIST data set of handwritten digit images [7], a purposefully constructed superset of the original MNIST data set [73]. |
| Dataset Splits | No | The paper mentions a test set and training examples but does not explicitly state a validation dataset split or its size/percentage. |
| Hardware Specification | Yes | In total we purchased several hundred CPU-hours on an externally managed cluster of Intel Xeon Gold 6132 processors. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for its experiments. |
| Experiment Setup | Yes | We initialized the parameters of the linear model with zero values and those of the Do S models with small random values. Specifically, we sampled the elements of U and V from a zero-mean normal distribution whose variance was inversely proportional to the number of elements in these matrices. |