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..
Privately Learning Smooth Distributions on the Hypercube by Projections
Authors: Clément Lalanne, Sébastien Gadat
ICML 2024 | Venue PDF | 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. |