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].
Preventing Disparate Treatment in Sequential Decision Making
Authors: Hoda Heidari, Andreas Krause
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on two real-world data sets illustrate and confirm this finding in practice. |
| Researcher Affiliation | Academia | Hoda Heidari1 and Andreas Krause1 1ETH Z urich EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: CFTL |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is openly available. |
| Open Datasets | Yes | Datasets We ran lasso regression on the normalized Crime and Communities data set [Dheeru and Karra Taniskidou, 2017]. [...] We also ran logistic regression on a classification data set the Adult Income data set [Dheeru and Karra Taniskidou, 2017] and observed very similar trends. (The citation points to UCI machine learning repository, 2017) |
| Dataset Splits | Yes | λ was chosen by performing a 10-fold cross validation on the entire data set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions running 'LASSO' and 'logistic regression', which are methods, but does not specify any software libraries or packages with their version numbers that would be needed to reproduce the experiments. |
| Experiment Setup | Yes | For the regression task, we ran LASSO with regularization coefficients λ = 0.01. λ was chosen by performing a 10-fold cross validation on the entire data set. We then used the same value of λ at all time steps. |