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..
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Authors: Liang Ding, Rui Tuo, Shahin Shahrampour
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments on benchmark datasets verify the superiority of EOF over the state-of-the-art in kernel approximation. |
| Researcher Affiliation | Academia | The authors are with Wm Michael Barnes 64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA. |
| Pseudocode | Yes | Algorithm 1 Entropic Optimal Features (EOF) |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is being released or provide a link to a code repository. |
| Open Datasets | Yes | Benchmark Algorithm: We now compare EOF with the following benchmark algorithms on several datasets from the UCI Machine Learning Repository: |
| Dataset Splits | Yes | In Table 1, we report the number of training samples Ntrain and test samples Ntest used for each dataset. |
| Hardware Specification | Yes | The run time is obtained on a Macbook Pro with a 4-core, 3.3 GHz Intel Core i5 CPU and 8 GB of RAM (2133Mhz). |
| Software Dependencies | No | The paper mentions 'Matlab' in the context of efficient matrix operations but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes data standardization and selection of feature sets, but does not provide specific hyperparameter values for model training, such as learning rates, batch sizes, or optimizer settings. |