Multiclass versus Binary Differentially Private PAC Learning
Authors: Satchit Sivakumar, Mark Bun, Marco Gaboardi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our focus is on theoretical questions aimed at uncovering general principles about when private learning is feasible. |
| Researcher Affiliation | Academia | Mark Bun Department of Computer Science Boston University Boston, MA 02215 mbun@bu.edu Marco Gaboardi Department of Computer Science Boston University Boston, MA 02215 gaboardi@bu.edu Satchit Sivakumar Department of Computer Science Boston University Boston, MA 02215 satchit@bu.edu |
| Pseudocode | No | The paper includes Figure 1 which is a diagram illustrating the algorithm, but it does not provide structured pseudocode or an algorithm block formatted like code. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets, therefore no training data is used or mentioned as publicly available. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, therefore no dataset splits for training, validation, or testing are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used for computation. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not include any experimental setup details such as hyperparameters or training configurations. |