152 citations · 155 across the 9 of their papers we have counts for
9 papers
The Branch-and-Bound Tree Closure
Marius Roland, Nagisa Sugishita, Alexandre Forel +3
This paper investigates the a-posteriori analysis of Branch-and-Bound~(BB) trees to extract structural information about the feasible region of mixed-binary linear programs. We int…
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…
The Differentiable Feasibility Pump
Matteo Cacciola, Alexandre Forel, Antonio Frangioni +1
Although nearly 20 years have passed since its conception, the feasibility pump algorithm remains a widely used heuristic to find feasible primal solutions to mixed-integer linear…
Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles
Youssouf Emine, Alexandre Forel, Idriss Malek +1
Tree ensembles, including boosting methods, are highly effective and widely used for tabular data. However, large ensembles lack interpretability and require longer inference times…
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…
Adaptive Partitioning for Chance-Constrained Problems with Finite Support
Marius Roland, Alexandre Forel, Thibaut Vidal
This paper studies chance-constrained stochastic optimization problems with finite support. It presents an iterative method that solves reduced-size chance-constrained models obtai…
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana, Abhilash Chenreddy, Erick Delage +3
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to so…
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…