Explainable Data-Driven Optimization: From Context to Decision and Back Again
Authors: Alexandre Forel, Axel Parmentier, Thibaut Vidal
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the value of our methods to explain data-driven decisions in repeated experiments with synthetic and real-world data. |
| Researcher Affiliation | Academia | 1CIRRELT & SCALE-AI Chair in Data-Driven Supply Chaims, Department of Mathematical and Industrial Engineering, Polytechnique Montreal, Montreal, Canada 2CERMICS, École des Ponts, Marne-la-Vallée, France. |
| Pseudocode | Yes | Algorithm 1 Iterative procedure for absolute explanations |
| Open Source Code | Yes | The code used to generate all the results in this paper is publicly available at https://github. com/alexforel/Explainable-CSO under an MIT license. |
| Open Datasets | Yes | Multi-item newsvendor. This problem is adapted from Kallus & Mao (2022). Two-stage shipment planning. This problem is taken from Bertsimas & Kallus (2020). (CVa R) shortest path. The problem is adapted from Elmachtoub et al. (2020) and Elmachtoub & Grigas (2022). Historical observations of the uncertain travel times are available on the edge level. We randomly sample n = 1000 past observations and train a random forest predictor. |
| Dataset Splits | No | The paper mentions 'The training data {(xi, yi)}n i=1 [...] is resampled in each experiment' and 'We randomly sample n = 1000 past observations and train a random forest predictor', but it does not specify explicit training/validation/test splits or a dedicated validation methodology. |
| Hardware Specification | Yes | All experiments are run on four cores of an Intel(R) Xeon(R) Gold 6336Y CPU at 2.40 GHz. |
| Software Dependencies | Yes | The simulations are implemented in Python 3.9.13. Gurobi 9.5.1 is used to solve all mixed-integer programming problems. [...] We use the standard procedure of scikit-learn v.1.0.2. |
| Experiment Setup | Yes | We use the standard procedure of scikit-learn v.1.0.2. with T = 100 trees (default value) and a maximum depth of 4. We focus on nearest-neighbor predictors with k = 10. All distances are measured through the l1 norm... |