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..
Margins, Kernels and Non-linear Smoothed Perceptrons
Authors: Aaditya Ramdas, Javier Peña
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive an accelerated smoothed algorithm with a convergence rate of log n / ρ given n separable points, which is strikingly similar to the classical kernelized Perceptron algorithm whose rate is 1/ρ^2. When no such classifier exists, we prove a version of Gordan s separation theorem for RKHSs, and give a reinterpretation of negative margins. |
| Researcher Affiliation | Academia | Aaditya Ramdas EMAIL Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213 USA Javier Pe na EMAIL Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213 USA |
| Pseudocode | Yes | Algorithm 1 Perceptron ... Algorithm 2 Normalized Perceptron ... Algorithm 3 Normalized Kernel Perceptron (NKP) ... Algorithm 4 Smoothed Normalized Kernel Perceptron ... Algorithm 5 Normalized Von-Neumann (NVN) ... Algorithm 6 Smoothed Normalized Kernel Perceptron Von Neumann (SNKPV N(q, δ)) ... Algorithm 7 Iterated Smoothed Normalized Kernel Perceptron-Von Neumann (ISNKPV N(γ, ϵ)) |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical contributions and does not mention the use of any datasets for training or evaluation. Therefore, no information about public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (training, validation, test) for reproducibility. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the specific hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers required for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |