118 citations · 200 across the 58 of their papers we have counts for
9 papers · 1 filter
Fairness Increases Adversarial Vulnerability
Cuong Tran, Keyu Zhu, Ferdinando Fioretto +1
The remarkable performance of deep learning models and their applications in consequential domains (e.g., facial recognition) introduces important challenges at the intersection of…
Differentiable Model Selection for Ensemble Learning
James Kotary, Vincenzo Di Vito, Ferdinando Fioretto
Model selection is a strategy aimed at creating accurate and robust models. A key challenge in designing these algorithms is identifying the optimal model for classifying any parti…
Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
Convex optimization finds many real-life applications, where--optimized on real data--optimization results may expose private data attributes (e.g., individual health records, comm…
Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support
Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto
The application of deep learning methods to speed up the resolution of challenging power flow problems has recently shown very encouraging results. However, power system dynamics a…
Pruning has a disparate impact on model accuracy
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim +1
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that prunin…
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
Cuong Tran, Keyu Zhu, Ferdinando Fioretto +1
A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure no…
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck +1
This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It reviews the conditions under which privacy and fairness may have aligned or contras…
Deadwooding: Robust Global Pruning for Deep Neural Networks
Sawinder Kaur, Ferdinando Fioretto, Asif Salekin
The ability of Deep Neural Networks to approximate highly complex functions is key to their success. This benefit, however, comes at the expense of a large model size, which challe…
Post-processing of Differentially Private Data: A Fairness Perspective
Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck
Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affectin…