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..
The Performance Analysis of Generalized Margin Maximizers on Separable Data
Authors: Fariborz Salehi, Ehsan Abbasi, Babak Hassibi
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are validated by extensive simulation results across a range of parameter values, problem instances, and model structures. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, California Institute of Technology, Pasadena, California, USA. Correspondence to: Fariborz Salehi <EMAIL>. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper describes that data for simulations is 'generated from a logistic-type model with the underlying parameter w Rp' and 'drawn independently from the Gaussian distribution'. It does not refer to or provide access to a pre-existing public dataset. |
| Dataset Splits | No | The paper does not specify any train/validation/test dataset splits. It discusses 'training data' and 'test data' but not their partitioning or sizes. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for conducting the numerical simulations. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | The empirical result is the average over 20 trials with p = 150... (Figure 1 caption). For the numerical simulations, the result is the average over 100 independent trials with p = 200 and Îș = 2. (Figure 2 caption). The empirical result is the average over 100 trials with p = 200, s = 0.1, and Îș = 2. (Figure 3 caption). |