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..
Differentially Private Image Classification by Learning Priors from Random Processes
Authors: Xinyu Tang, Ashwinee Panda, Vikash Sehwag, Prateek Mittal
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We attain new state-of-the-art accuracy when training from scratch on CIFAR10, CIFAR100, Med MNIST and Image Net for a range of privacy budgets ε [1, 8]. In particular, we improve the previous best reported accuracy on CIFAR10 from 60.6% to 72.3% for ε = 1. |
| Researcher Affiliation | Academia | Xinyu Tang Ashwinee Panda Vikash Sehwag Prateek Mittal Princeton University |
| Pseudocode | No | The paper describes the three phases of its approach (Phase I, II, III) and shows a pipeline in Figure 1, but it does not include a formal pseudocode block or an algorithm labeled as such. |
| Open Source Code | Yes | Our code is available at https://github.com/inspire-group/DP-Rand P. |
| Open Datasets | Yes | We evaluate DP-Rand P on CIFAR10/CIFAR100 [41], Derma MNIST in Med MNIST [65, 66] and private linear probing version of DP-Rand P on Image Net [16]. |
| Dataset Splits | Yes | We follow Hölzl et al. [34] and report the validation accuracy of Derma MNIST in Tab. 3. Here we also report the test accuracy in Tab. 16 and we can see DP-Rand P outperforms the DP-SGD baseline. |
| Hardware Specification | Yes | A single run to privately train a WRN-16-4 for CIFAR10 takes around 5.5 hours for 875 steps with 1 A100 GPU in our evaluation. |
| Software Dependencies | No | We use the Opacus library [67] for the DP-SGD implementation. [...] The paper mentions 'Opacus library' with a citation to an arXiv preprint, but does not provide a specific version number for Opacus or any other software dependency like PyTorch. |
| Experiment Setup | Yes | Hyperparameters. Tab. 13, 14 and 15 summarize the hyperparameters for DP-Rand P on CIFAR10, CIFAR100 and Derma MNIST respectively. |