The Performance Analysis of Generalized Margin Maximizers on Separable Data
Authors: Fariborz Salehi, Ehsan Abbasi, Babak Hassibi
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 <fsalehi@caltech.com>. |
| 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 κ = 2. (Figure 2 caption). The empirical result is the average over 100 trials with p = 200, s = 0.1, and κ = 2. (Figure 3 caption). |