4 papers
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…
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
Kai Jungel, Axel Parmentier, Maximilian Schiffer +1
Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and tr…
Regularization and Optimization in Model-Based Clustering
Raphael Araujo Sampaio, Joaquim Dias Garcia, Marcus Poggi +1
Due to their conceptual simplicity, k-means algorithm variants have been extensively used for unsupervised cluster analysis. However, one main shortcoming of these algorithms is th…
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…