42 citations · 55 across the 6 of their papers we have counts for
12 papers
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck
A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ens…
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
Minas Chatzos, Ferdinando Fioretto, Terrence W. K. Mak +1
The AC Optimal Power Flow (AC-OPF) is a key building block in many power system applications. It determines generator setpoints at minimal cost that meet the power demands while sa…
Differentially Private Convex Optimization with Feasibility Guarantees
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.…
Differentially Private Optimal Power Flow for Distribution Grids
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
Although distribution grid customers are obliged to share their consumption data with distribution system operators (DSOs), a possible leakage of this data is often disregarded in…
Lagrangian Duality for Constrained Deep Learning
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak +3
This paper explores the potential of Lagrangian duality for learning applications that feature complex constraints. Such constraints arise in many science and engineering domains,…
PPSM: A Privacy-Preserving Stackelberg Mechanism: Privacy Guarantees for the Coordination of Sequential Electricity and Gas Markets
Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck
This paper introduces a differentially private mechanism to protect the information exchanged during the coordination of the sequential market-clearing of electricity and natural g…