8 citations · 31 across the 20 of their papers we have counts for
5 papers · 1 filter
DistrictNet: Decision-aware learning for geographical districting
Cheikh Ahmed, Alexandre Forel, Axel Parmentier +1
Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts. In logistics, it is a major strategic decision determining op…
CF-OPT: Counterfactual Explanations for Structured Prediction
Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier +1
Optimization layers in deep neural networks have enjoyed a growing popularity in structured learning, improving the state of the art on a variety of applications. Yet, these pipeli…
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Alexandre Forel, Axel Parmentier, Thibaut Vidal
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is…
Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles
Alexandre Forel, Axel Parmentier, Thibaut Vidal
Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier. Obtaining robust counterfactual explanations is essential…
Optimal Counterfactual Explanations in Tree Ensembles
Axel Parmentier, Thibaut Vidal
Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions. The absence of guarantees of performance and robustness…