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].
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
Authors: Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon
NeurIPS 2020 | Venue PDF | 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. EMAIL Jianfeng Chi Department of Computer Science University of Virginia EMAIL Yuan Tian Department of Computer Science University of Virginia EMAIL Geoffrey J. Gordon Carnegie Mellon University Microsoft Research Montreal EMAIL |
| 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. |