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..
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Authors: Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments suggest that our algorithms can effectively deal with the challenges caused by data irregularity.Finally, we test our proposed aglorithms on both synthetic and real-world datasets. Experimental results are consistent with our theoretical claims and reveal the effectiveness of our algorithms in handling heavy-tailed datasets. |
| Researcher Affiliation | Academia | Di Wang * 1 2 Hanshen Xiao * 3 Srini Devadas 3 Jinhui Xu 1 1Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 2King Abdullah University of Science and Technology, Thuwal, Saudi Arabia 3CSAIL, MIT, Cambridge, MA. |
| Pseudocode | Yes | Algorithm 1 Sample-aggregate Framework (Nissim et al., 2007) Algorithm 2 Mechanism M in (Bun & Steinke, 2019) Algorithm 3 Heavy-tailed DP-SCO with known mean Algorithm 4 Heavy-tailed DP-SCO with known variance |
| Open Source Code | Yes | Due to the space limit, some definitions, all the proofs are relegated to the appendix in the Supplementary Material, which also includes the codes of experiments. |
| Open Datasets | Yes | For real-world data, we use the Adult dataset from the UCI Repository (Dua & Graff, 2017). |
| Dataset Splits | No | The paper only specifies a training and testing split ("28,000 amongst which are used as the training set and the rest are used for test") but does not explicitly mention a validation set or how it would be used for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For the privacy parameters, we will choose ϵ = {0.1, 0.5, 1} and δ = O( 1 /n). See Appendix for the selections of other parameters. |