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..
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
Authors: Christopher A. Choquette-Choo, Hugh Brendan Mcmahan, J Keith Rush, Abhradeep Guha Thakurta
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical evaluation on both examplelevel DP for image classification and user-level DP for language modeling demonstrate substantial improvements over all previous methods, including the widely-used DP-SGD. |
| Researcher Affiliation | Industry | 1Google Research. Correspondence to: <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DP-Prefix Sum Computation via FFT (with d = 1) |
| Open Source Code | Yes | Our code is at: https://github.com/google-research/ federated/tree/master/multi_epoch_dp_ matrix_factorization. |
| Open Datasets | Yes | We train image classification models on CIFAR10 (Krizhevsky, 2009)... and We use the standard benchmark: Stack Overflow next-word prediction (Reddi et al., 2020). |
| Dataset Splits | Yes | We train image-classification models using the CIFAR10 dataset as hosted in tensorflow-datasets, containing 50,000 training and 10,000 test examples. |
| Hardware Specification | No | The paper mentions "V100 GPU" in the context of computational cost for a specific component (optimal FFT decoder) but does not provide specific hardware details for running its main experiments. |
| Software Dependencies | No | The paper mentions "tensorflow-datasets" and "NumPy" (in Appendix K), but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Models trained for 20 epochs on CIFAR10 with a batch size of 500. We sweep over learning rates of values (1 10i, 2 10i, 5 10i) for i in { 2, 1}; We sweep over momentum values of 0, 0.85, 0.9, 0.95. |