7 citations · 18 across the 17 of their papers we have counts for
6 papers · 1 filter
Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex Programming
Ben Wooding, Simone Garatti, Marco C. Campi +1
The scenario approach is a well-established statistical framework for data-driven decision-making. In particular, in data-driven optimization, the scenario approach unveils how the…
Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem
Marco C. Campi, Simone Garatti
This paper illustrates the Pick-to-Learn methodology applied to the calibration of a Model Predictive Control policy. While developed around a specific example, the presentation is…
Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting
Laura Lützow, Simone Garatti, Marco C. Campi +2
Conformal prediction constructs prediction sets with finite-sample coverage guarantees, but its calibration stage is structurally constrained to a scalar score function and a singl…
Scenario theory for multi-criteria data-driven decision making
Simone Garatti, Lucrezia Manieri, Alessandro Falsone +3
The scenario approach provides a powerful data-driven framework for designing solutions under uncertainty with rigorous probabilistic robustness guarantees. Existing theory, howeve…
Robustness certificates in data-driven non-convex optimization with additively-uncertain constraints
Alexander J Gallo, Massimiliano Zoggia, Alessandro Falsone +2
We consider decision-making problems that are formulated as non-convex optimization programs where uncertainty enters the constraints through an additive term, independent of the d…
Scenario Approach with Post-Design Certification of User-Specified Properties
Algo Carè, Marco C. Campi, Simone Garatti
The scenario approach is an established data-driven design framework that comes equipped with a powerful theory linking design complexity to generalization properties. In this appr…