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..
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Authors: Yingxue Zhou, Steven Wu, Arindam Banerjee
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate PDP-SGD on training neural networks with two datasets: the MNIST (Le Cun et al., 1998) and Fashion MNIST (Xiao et al., 2017). We compare the performance of PDP-SGD with the baseline DP-SGD for various privacy levels ϵ. In addition, we also explore a heuristic method, i.e., DP-SGD with random projection by replacing the projector with a Rk p Gaussian random projector (Bingham and Mannila, 2001; Blocki et al., 2012). We present the experimental results after discussing the experimental setup. More details and additional results are in Appendix D. |
| Researcher Affiliation | Academia | Yingxue Zhou , Zhiwei Steven Wu , Arindam Banerjee Department of Computer Science & Engineering, University of Minnesota School of Computer Science, Carnegie Mellon University. Department of Computer Science, University of Illinois Urbana-Champaign |
| Pseudocode | Yes | Algorithm 1 Projected DP-SGD (PDP-SGD) |
| Open Source Code | No | The paper does not explicitly provide access to the source code for the methodology described in the paper. |
| Open Datasets | Yes | We empirically evaluate PDP-SGD on training neural networks with two datasets: the MNIST (Le Cun et al., 1998) and Fashion MNIST (Xiao et al., 2017). |
| Dataset Splits | No | The paper describes training and test sets and a small public dataset but does not explicitly provide details about a separate validation dataset split. |
| Hardware Specification | Yes | All experiments have been run on NVIDIA Tesla K40 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The mini-batch size is set to be 250 for both MNIST and Fashion MNIST. For the step size, we follow the grid search method with search space {0.01, 0.05, 0.1, 0.2} to tune the step size for MNIST and the search space is {0.01, 0.02, 0.05, 0.1, 0.2} for Fashion MNIST. We choose the step size based on the training accuracy at the last epoch. [...] For training, a fixed budget on the number of epochs i.e., 30 is assigned for the each task. [...] We choose gradient clip size to be 1.0 for both datasets. |