Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
Authors: Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two real-world datasets to corroborate the inference guarantees and validate this trade-off. |
| Researcher Affiliation | Collaboration | Han Zhao D. E. Shaw & Co. han.zhao@cs.cmu.edu Jianfeng Chi Department of Computer Science University of Virginia jc6ub@virginia.edu Yuan Tian Department of Computer Science University of Virginia yuant@virginia.edu Geoffrey J. Gordon Carnegie Mellon University Microsoft Research Montreal geoff.gordon@microsoft.com |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | (1) Adult dataset [8]: The Adult dataset is a benchmark dataset for classification. (2) UTKFace dataset [38]: The UTKFace dataset is a large-scale face benchmark dataset containing more than 20,000 images with annotations of age, gender, and ethnicity. [8] links to UCI machine learning repository, and [38] refers to a published paper from which the dataset originates. |
| Dataset Splits | No | The paper states, 'We refer readers to Sec. C in the appendix for detailed descriptions about the data pre-processing pipeline and the data distribution for each dataset.' However, it does not provide specific split percentages or sample counts for training, validation, or test sets within the main text. |
| Hardware Specification | No | The acknowledgements mention 'a Nvidia GPU grant' but do not specify any particular GPU model, CPU, or other hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, or specific libraries with versions). |
| Experiment Setup | No | The paper mentions 'Sec. C in the appendix provides detailed descriptions about the methods and the hyperparameter settings.' However, it does not provide these specific experimental setup details, hyperparameters, or system-level training settings within the main text. |