Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Strategic Classification in the Dark
Authors: Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, Nir Rosenfeld
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,621 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,081 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with experiments on synthetic data as well as on a large dataset of loan requests. |
| Researcher Affiliation | Academia | Ganesh Ghalme * 1 Vineet Nair * 1 Itay Eilat 1 Inbal Talgam-Cohen 1 Nir Rosenfeld 1 *Equal contribution 1Technion Israel Institute of Technology. Correspondence to: Ganesh Ghalme <EMAIL>, Vineet Nair <EMAIL>. |
| Pseudocode | No | The paper describes algorithms verbally and mathematically, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Code is publicly available at https://github.com/ staretgicclfdark/strategic_rep. |
| Open Datasets | Yes | We now turn to studying the Prosper loans dataset. ... The data includes n = 20, 222 examples, which we partition 70 15 15 into three sets: a training set T for Jury, a held-out test-set S, and a pool of samples from which we sample points for each TC(x), x S. |
| Dataset Splits | Yes | The data includes n = 20, 222 examples, which we partition 70 15 15 into three sets: a training set T for Jury, a held-out test-set S, and a pool of samples from which we sample points for each TC(x), x S. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'SVM' and the 'algorithm of Hardt et al. (2016)' but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | No | The paper describes the general experimental setting and the models used, but it does not provide specific hyperparameters or system-level training settings like learning rates, batch sizes, or number of epochs. |