7 citations · 18 across the 17 of their papers we have counts for
19 papers
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…
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…
Risk Analysis and Design Against Adversarial Actions
Marco C. Campi, Algo Carè, Luis G. Crespo +2
Learning models capable of providing reliable predictions in the face of adversarial actions has become a central focus of the machine learning community in recent years. This chal…
Compression, Generalization and Learning
Marco C. Campi, Simone Garatti
A compression function is a map that slims down an observational set into a subset of reduced size, while preserving its informational content. In multiple applications, the condit…
On the sensitivity of linear resource sharing problems to the arrival of new agents
Alessandro Falsone, Kostas Margellos, Jacopo Zizzo +2
We consider a multi-agent optimal resource sharing problem that is represented by a linear program. The amount of resource to be shared is fixed, and agents belong to a population…
A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines
Marco C. Campi, Simone Garatti
In this paper we consider optimization with relaxation, an ample paradigm to make data-driven designs. This approach was previously considered by the same authors of this work in G…
An incremental scenario approach for building energy management with uncertain occupancy
Arman Karshenas, Kostas Margellos, Simone Garatti
We deal with the problem of energy management in buildings subject to uncertain occupancy. To this end, we formulate this as a finite horizon optimization program and optimize with…
Scenario-based Economic Dispatch with Uncertain Demand Response
Hao Ming, Le Xie, Marco Campi +2
This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A ce…
Energy management for building district cooling: a distributed approach to resource sharing
Fabio Belluschi, Alessandro Falsone, Daniele Ioli +3
This paper deals with energy management in a district where multiple buildings can communicate over a time-varying network and aim at optimizing the use of shared resources like st…
A relaxation technique to ensure feasibility in stochastic control with input and state constraints
Luca Deori, Simone Garatti, Maria Prandini
We consider a stochastic linear system and address the design of a finite horizon control policy that is optimal according to some average cost criterion and accounts also for prob…
Finite time distributed averaging over ring networks
Alessandro Falsone, Kostas Margellos, Simone Garatti +1
We consider a multi-agent system where each agent has its own estimate of a given quantity and the goal is to reach consensus on the average. To this purpose, we propose a distribu…
Dual decomposition for multi-agent distributed optimization with coupling constraints
Alessandro Falsone, Kostas Margellos, Simone Garatti +1
We study distributed optimization in a cooperative multi-agent setting, where agents have to agree on the usage of shared resources and can communicate via a time-varying network t…
Distributed constrained optimization and consensus in uncertain networks via proximal minimization
Kostas Margellos, Alessandro Falsone, Simone Garatti +1
We provide a unifying framework for distributed convex optimization over time-varying networks, in the presence of constraints and uncertainty, features that are typically treated…
Performance assessment and design of abstracted models for stochastic hybrid systems through a randomized approach
M. Prandini, S. Garatti, R. Vignali
In this paper, a simulation-based method for the analysis and design of abstracted models for a stochastic hybrid system is proposed. The accuracy of a model is evaluated in terms…