7 papers
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…
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…
Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees
Dario Paccagnan, Daniel Marks, Marco C. Campi +1
Data-driven methods have become paramount in modern systems and control problems characterized by growing levels of complexity. In safety-critical environments, deploying these met…
A Scenario-Based Approach for Stochastic Economic Model Predictive Control with an Expected Shortfall Constraint
Alireza Arastou, Algo Carè, Ye Wang +2
This paper presents a novel approach to stochastic economic model predictive control (SEMPC) that minimizes average economic cost while satisfying an empirical expected shortfall (…