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..
Private Stochastic Convex Optimization and Sparse Learning with Heavy-tailed Data Revisited
Authors: Youming Tao, Yulian Wu, Xiuzhen Cheng, Di Wang
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DPSCO) with heavy-tailed data... We propose a novel robust and private mean estimator which is optimal. Based on its idea, we then extend to the general d-dimensional space and study DP-SCO... We also provide lower bounds... We propose a new method and show it is also optimal... |
| Researcher Affiliation | Academia | Youming Tao1 , Yulian Wu2 , Xiuzhen Cheng1 and Di Wang2 1School of Computer Science, Shandong University 2CEMSE, KAUST EMAIL |
| Pseudocode | Yes | Algorithm 1 Truncation Based DP Mean Estimator: DPODME TΟ΅,Ξ΄,ΞΎ(X) |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not mention specific datasets or their public availability for training purposes. It refers to "data samples X" without specifying a publicly accessible dataset source or name. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore it does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, thus no specific hardware details used for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments, thus no specific ancillary software details with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs, therefore it does not provide specific experimental setup details, hyperparameters, or training configurations. |