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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Deep Learning with Label Differential Privacy
Authors: Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement the empirical results with theoretical analysis showing that Label DP is provably easier than protecting both the inputs and labels. We present a novel multi-stage algorithm (LP-MST) for training deep neural networks with Label DP that builds on top of RRWith Prior (see Section 3 and Algorithm 3), and we benchmark its empirical performance (Section 5) on multiple datasets, domains, and architectures, including the following. |
| Researcher Affiliation | Collaboration | Badih Ghazi Google Research EMAIL Noah Golowich EECS, MIT EMAIL Ravi Kumar Google Research EMAIL Pasin Manurangsi Google Research EMAIL Chiyuan Zhang Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 RRTop-k; Algorithm 2 RRWith Prior; Algorithm 3 Multi-Stage Training (LP-MST) |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it explicitly state that the source code for its methodology is being released. |
| Open Datasets | Yes | We evaluate RRWith Prior on standard benchmark datasets that have been widely used in previous works on private machine learning. Specifically, CIFAR-10 [60] is a 10-class image classification benchmark dataset. In Table 2 we also show results on CIFAR-100, which is a more challenging variant with 10 more classes. In addition, we also evaluate on Movie Lens-1M [49], which contains 1 million anonymous ratings of approximately 3, 900 movies, made by 6,040 Movie Lens users. |
| Dataset Splits | Yes | Following [15], we randomly split the data into 80% train and 20% test |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper states, 'Please see the Supplementary Material for full details on the datasets and the experimental setup.' However, it does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |