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..
A Randomized Approach to Tight Privacy Accounting
Authors: Jiachen (Tianhao) Wang, Saeed Mahloujifar, Tong Wu, Ruoxi Jia, Prateek Mittal
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical evaluation shows the proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning. (Abstract) / 6 Numerical Experiments: In this section, we conduct numerical experiments to illustrate (1) EVR paradigm with MC verifiers enables a tighter privacy analysis, and (2) MC accountant achieves state-of-the-art performance in privacy parameter estimation. |
| Researcher Affiliation | Academia | Jiachen T. Wang Princeton University EMAIL Saeed Mahloujifar Princeton University EMAIL Tong Wu Princeton University EMAIL Ruoxi Jia Virginia Tech EMAIL Prateek Mittal Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1 Estimate-Verify-Release (EVR) Framework / Algorithm 2 DPV(M, ε, δest) with Simple MC Estimator and Offset Parameter . / Algorithm 3 MC Accountant for εY (δ). |
| Open Source Code | No | The paper does not include any explicit statements or links indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | To further underscore the superiority of the EVR paradigm in practical applications, we illustrate the privacy-utility tradeoff curve when finetuning on CIFAR100 dataset with DP-SGD. (Section 6.1) / Image Net-pretrained BEi T [4] on CIFAR100 (Figure 4, H.2.1, H.2.2) |
| Dataset Splits | No | The paper mentions using CIFAR100 and ImageNet for training but does not explicitly state the specific training/validation/test splits (e.g., percentages or sample counts) needed for reproduction. It refers to following existing training procedures but does not detail the splits. |
| Hardware Specification | Yes | The runtime is estimated on an NVIDIA A100-SXM4-80GB GPU. (Figure 3 (b) caption) / when using a NVIDIA A100-SXM4-80GB GPU (Section H.1) |
| Software Dependencies | No | Section H.1 mentions 'Py Torch s CUDA functionality' and 'torch.randn' but does not specify exact version numbers for PyTorch, CUDA, or other key software components. |
| Experiment Setup | Yes | For DP-GD training, we set σ as 28.914, clipping norm as 1, learning rate as 2, and we train for at most 60 iterations, and we only finetune the last layer on CIFAR-100. (Section H.2.1) / For DP-SGD training, we set σ as 5.971, clipping norm as 1, learning rate as 0.2, momentum as 0.9, batch size as 4096, and we train for at most 360 iterations (30 epochs). (Section H.2.2) / For Figure 1 & Figure 3, the FFT-based method has hyperparameter being set as εerror = 10 3, δerror = 10 10. (Section H.2.1) |