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 [1].
Learning Robust and Privacy-Preserving Representations via Information Theory
Authors: Binghui Zhang, Sayedeh Leila Noorbakhsh, Yun Dong, Yuan Hong, Binghui Wang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations We evaluate ARPRL on both synthetic and real-world datasets. The results on the synthetic dataset is for visualization and verifying the tradeoff purpose. Experimental Setup We train the neural networks via Stochastic Gradient Descent (SGD)... Results. Tables 1 shows the results on the three datasets, where we report the robust accuracy (under the l attack), normal test accuracy, and attribute inference accuracy (as well as the gap to random guessing). |
| Researcher Affiliation | Academia | 1Illinois Institute of Technology 2Milwaukee School of Engineering 3University of Connecticut EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Figure 1 overviews our ARPRL. Algorithm 1 in Appendix details the training of ARPRL. |
| Open Source Code | Yes | Code&Full Report https://github.com/ARPRL/ARPRL |
| Open Datasets | Yes | We use three real-world datasets from different applications, i.e., the widely-used Celeb A (Liu et al. 2015) image dataset (150K training images and 50K for testing), the Loans (Hardt, Price, and Srebro 2016), and Adult Income (Dua and Graff 2017) datasets |
| Dataset Splits | Yes | Each circle indicates a class and has 5,000 samples, where 80% of the samples are for training and the rest 20% for testing. ... Celeb A (Liu et al. 2015) image dataset (150K training images and 50K for testing) |
| Hardware Specification | Yes | We implement ARPRL in Py Torch and use the NSF Chameleon Cloud GPUs (Keahey et al. 2020) (Cent OS7CUDA 11 with Nvidia Rtx 6000) to train the model. |
| Software Dependencies | Yes | We implement ARPRL in Py Torch and use the NSF Chameleon Cloud GPUs (Keahey et al. 2020) (Cent OS7CUDA 11 with Nvidia Rtx 6000) to train the model. |
| Experiment Setup | Yes | We train the neural networks via Stochastic Gradient Descent (SGD), where the batch size is 100 and we use 10 local epochs and 50 global epochs in all datasets. The learning rate in SGD is set to be 1e 3. |