Information Discrepancy in Strategic Learning
Authors: Yahav Bechavod, Chara Podimata, Steven Wu, Juba Ziani
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical analysis with experiments on real-world datasets. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, The Hebrew University. 2School of Engineering and Applied Sciences, Harvard University. 3School of Computer Science, Carnegie Mellon University. 4School of Industrial and Systems Engineering, Georgia Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 Principal-Agent Interaction Protocol |
| Open Source Code | Yes | Our code is available in the supplementary. |
| Open Datasets | Yes | Available at https://archive.ics.uci.edu/ ml/datasets/default+of+credit+card+clients & https://archive.ics.uci.edu/ml/datasets/ adult. |
| Dataset Splits | No | The paper describes data preprocessing steps, group creation, and how projection matrices are obtained (e.g., "running SVD on the points inside of G1, G2"), but it does not provide explicit details about train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any particular software dependencies with version numbers (e.g., programming languages, libraries, frameworks) used for the implementation of the work. |
| Experiment Setup | Yes | In both cases, we ran ERM in order to identify w and we assumed that costs are A1 = A2 = Id d. [...] To obtain the projection matrices Π1, Π2, we ran SVD on the points inside of G1, G2. [...] Vg,5 correspond to the matrix having as columns the eigenvectors corresponding to the 5 top eigenvalues and zeroed out all other d 5 columns. |