Privately Learning Smooth Distributions on the Hypercube by Projections
Authors: Clément Lalanne, Sébastien Gadat
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The contributions of this article are two-fold: Firstly, it generalizes the one-dimensional results of (Lalanne et al., 2023b) to non-integer levels of smoothness and to a high-dimensional setting... Secondly, this article presents a private strategy of estimation that is data-driven... This is achieved by adapting the Lepskii method for private selection, by adding a new penalization term that makes the estimation privacy-aware. |
| Researcher Affiliation | Academia | 1Toulouse School of Ecolomics, Universit e Toulouse 1 Capitole, Toulouse, France. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It is a theoretical paper and does not mention any code release. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets, therefore no public access information for a dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, so no specific dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup involving specific hardware or computational resources. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions required to replicate its findings. |
| Experiment Setup | No | The paper is theoretical and describes mathematical formulations and proofs rather than an empirical experimental setup with hyperparameters or training configurations. |