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..
Diving into the shallows: a computational perspective on large-scale shallow learning
Authors: SIYUAN MA, Mikhail Belkin
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Results |
| Researcher Affiliation | Academia | Siyuan Ma Mikhail Belkin Department of Computer Science and Engineering The Ohio State University EMAIL |
| Pseudocode | Yes | Algorithm: Eigen Pro(X, y, k, m, η, τ, M) |
| Open Source Code | Yes | In the second part of the paper we propose Eigen Pro iteration (see http://www.github.com/Eigen Pro for the code) |
| Open Datasets | Yes | Dataset Size Gaussian Laplace Cauchy Eig Pro Pega Eig Pro Pega Eig Pro Pega MNIST 6 104 ... CIFAR-10 5 104 ... SVHN 7 104 ... HINT-S 5 104 ... TIMIT 1 106 ... SUSY 4 106 |
| Dataset Splits | No | The paper mentions "train" and "test" data in tables but does not explicitly provide information on validation splits or methodology. |
| Hardware Specification | Yes | Experiments were run on a workstation with 128GB main memory, two Intel Xeon(R) E5-2620 CPUs, and one GTX Titan X (Maxwell) GPU. |
| Software Dependencies | No | The paper mentions software like Pegasos and Random Fourier Features but does not specify their version numbers or other ancillary software dependencies with versions. |
| Experiment Setup | Yes | For consistent comparison, all iterative methods use mini-batch of size m = 256. Eigen Pro preconditioner is constructed using the top k = 160 eigenvectors of a subsampled dataset of size M = 4800. For Eigen Pro-RF, we set the damping factor τ = 1/4. For primal Eigen Pro τ = 1. |