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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Authors: Alexandre Forel, Axel Parmentier, Thibaut Vidal
ICML 2023 | Venue PDF | 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... |