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..
LabelDP-Pro: Learning with Label Differential Privacy via Projections
Authors: Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that such projection-based algorithms can be made practical and that they improve on the state-of-the-art for label DP training in the high-privacy regime. We complement our empirical evaluation with theoretical results shedding light on the efficacy of our method through the lens of bias-variance trade-offs. |
| Researcher Affiliation | Collaboration | Badih Ghazi Google Research Yangsibo Huang Princeton University Pritish Kamath Google Research Ravi Kumar Google Research Pasin Manurangsi Google Research Chiyuan Zhang Google Research |
| Pseudocode | Yes | Algorithm 1: Label DP-Pro |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about releasing the source code for the methodology described. |
| Open Datasets | Yes | We compare our Label DP-Pro on four benchmark datasets commonly used in Label DP learning evaluations: MNIST (Le Cun et al., 1998), k MNIST (Clanuwat et al., 2018), Fashion MNIST (Xiao et al., 2017), and CIFAR-10 (Krizhevsky et al., 2009). |
| Dataset Splits | No | The paper does not provide explicit train/validation/test split percentages or absolute counts for all datasets. For LP-2ST baseline, it mentions: 'We use 40% of the data for the first stage and 60% for the second.' but this is not a general validation split. |
| Hardware Specification | Yes | Results for MNIST, CIFAR-10, and CIFAR10-Self SL are reported on a single NVIDIA Tesla-P100 GPU, while results for Criteo Attribution are reported on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions 'all implemented in JAX' and refers to PyTorch and TensorFlow for autodiff primitives, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Table 8: Hyper-parameters for our experimental results presented in the main paper. Optimizer: SGD, RMSprop; Learning rate: various values; # Epochs: various values; Batch size: various values; Clipping norm (DP-SGD & Label DP-Pro): various values. |