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..
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
Authors: Puning Zhao, Jiafei Wu, Zhe Liu, Chong Wang, Rongfei Fan, Qingming Li
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we explore algorithms achieving optimal rates of DP optimization with heavy-tailed gradients. Our first method is a simple clipping approach... We then propose an iterative updating method... Our results match the minimax lower bound, indicating that the theoretical limit of stochastic convex optimization under DP is achievable. |
| Researcher Affiliation | Academia | 1School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China 2 Ningbo University, Ningbo, China 3 Beijing Institute of Technology, Beijing, China 4 Zhejiang University, Hangzhou, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Stochastic optimization; Algorithm 2: Simple clipping method for mean estimation; Algorithm 3: Iterative updating method for mean estimation |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and algorithm design for convex optimization problems with heavy-tailed data. It does not describe experiments using any specific dataset, nor does it provide access information for any open datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on specific datasets. Therefore, it does not describe any training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical, focusing on algorithm design and proofs. It does not describe any experimental evaluations that would require specific hardware, hence no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not discuss the implementation of its proposed algorithms. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not present any experimental evaluations. Consequently, there are no details regarding experimental setup, hyperparameters, or system-level training settings. |