CF-OPT: Counterfactual Explanations for Structured Prediction
Authors: Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier, Thibaut Vidal
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical results show that both close and plausible explanations can be obtained for problems from the recent literature. |
| Researcher Affiliation | Academia | 1CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematical and Industrial Engineering, Polytechnique Montreal, Montreal, Canada 2CERMICS, École des Ponts, Marne-la-Vallée, France. Correspondence to: Germain Vivier-Ardisson <germain.vivier-ardisson@enpc.fr>. |
| Pseudocode | Yes | Our algorithm is given in Algorithm 1 in Appendix D. |
| Open Source Code | Yes | The code used to generate all the results in this paper is available publicly at https://github. com/GermainVivierArdisson/CF-OPT under an MIT license. |
| Open Datasets | Yes | We follow the experimental setting of Vlastelica et al. (2019) and used subsequently in several works (Dalle et al., 2022; Tang & Khalil, 2022; Mc Kenzie et al., 2023). The training set {xi, θi, yi}N i=1 is made of N = 10000 examples of Warcraft maps as well as their associated true costs and shortest paths. |
| Dataset Splits | No | The paper mentions 'All VAEs are trained until convergence, with early stopping to avoid overfitting', which implies a validation set was used, but it does not specify the size or percentage split for a validation dataset. |
| Hardware Specification | Yes | Our experiments are implemented in Python and run on four cores of an Intel Core i7-8565U CPU @ 1.80GHz and use 16GB RAM. |
| Software Dependencies | No | The paper mentions 'Python', 'Pytorch', 'Gurobi', and 'scipy.special package' as software used, but does not provide specific version numbers for these dependencies, making the description not fully reproducible. |
| Experiment Setup | Yes | We use a step size γ = 0.003, maximum number of iterations K = 3000, maximum number of non-improving iterations cmax = 50, and corresponding update tolerance u = 0.9. |