Interpretable and Differentially Private Predictions
Authors: Frederik Harder, Matthias Bauer, Mijung Park4083-4090
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the usefulness of our approach on several image benchmark datasets as well as a medical dataset.In this section we evaluate the trade-off between accuracy, privacy, and interpretability for our LLM model on several datasets and compare to other methods where it is appropriate. |
| Researcher Affiliation | Academia | Frederik Harder,1,2 Matthias Bauer,1,3* Mijung Park1,2 1Max Planck Institute for Intelligent Systems, T ubingen, Germany 2Department of Computer Science, University of T ubingen, T ubingen, Germany 3Department of Engineering, University of Cambridge, Cambridge, UK |
| Pseudocode | Yes | Algorithm 1 DP-LLM for interpretable classification |
| Open Source Code | Yes | Our implementation is available on Git Hub2. 2github.com/frhrdr/dp-llm |
| Open Datasets | Yes | MNIST (Le Cun and Cortes 2010) and Fashion-MNIST (Xiao, Rasul, and Vollgraf 2017) imagesand Henan Renmin Hospital Data (Li et al. 2017; Maxwell et al. 2017)4. It contains 110,300 medical records with 62 input features and 3 binary outputs. |
| Dataset Splits | No | We consider the classification of MNIST (Le Cun and Cortes 2010) and Fashion-MNIST (Xiao, Rasul, and Vollgraf 2017) images with the usual train/test splits and train a CNN3 as a baseline model...The paper mentions train/test splits but does not specify a validation split explicitly or a detailed methodology for creating all splits (e.g., percentages or counts). |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or specific cloud instances) used for running the experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8) were explicitly mentioned. |
| Experiment Setup | Yes | We train several LLMs in the private and nonprivate setting. By default, we use LLM models with M = 30 filters per class and random projections to D = 300 dimensions, which are optimized for 20 epochs using the Adam optimizer with learning rate 0.001, decreasing by 20% every 5 epochs. On MNIST the model benefits from a decreased inverse softmax temperature β = 1/30, while β = 1 is optimal for Fashion-MNIST. We choose a large batch size of 500, as this improves the signal-to-noise ratio of our algorithm. In the private setting we clip the persample gradient norm to C = 0.001 and train with σ = 1.3and By default, we use an LLM model with M = 2 filters per class and no random projections, which is optimized for 20 epochs using the Adam optimizer with learning rate 0.01, decreasing by 20% every 5 epochs. We choose a batch size of 256. In the private setting we clip the per-sample gradient norm to 0.001 and train with σ = 1.25. |