1 citations · 1 across the 7 of their papers we have counts for
7 papers
Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
Panagiotis D. Grontas, Antonio Terpin, Efe C. Balta +2
We introduce an output layer for neural networks that ensures satisfaction of convex constraints. Our approach, net, leverages operator splitting for rapid and reliable projecti…
Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design
Marino Kühne, Panagiotis D. Grontas, Giulia De Pasquale +3
Although social networks have expanded the range of ideas and information accessible to users, they are also criticized for amplifying the polarization of user opinions. Given the…
Operator Splitting for Convex Constrained Markov Decision Processes
Panagiotis D. Grontas, Anastasios Tsiamis, John Lygeros
We consider finite Markov decision processes (MDPs) with convex constraints and known dynamics. In principle, this problem is amenable to off-the-shelf convex optimization solvers,…
Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach
Colin Dirren, Mattia Bianchi, Panagiotis D. Grontas +2
We study the convex-concave bilinear saddle-point problem , where both, only one, or none of the functions and are strongly convex, a…
Designing Optimal Personalized Incentive for Traffic Routing using BIG Hype algorithm
Panagiotis D. Grontas, Carlo Cenedese, Marta Fochesato +3
We study the problem of optimally routing plug-in electric and conventional fuel vehicles on a city level. In our model, commuters selfishly aim to minimize a local cost that combi…
BIG Hype: Best Intervention in Games via Distributed Hypergradient Descent
Panagiotis D. Grontas, Giuseppe Belgioioso, Carlo Cenedese +3
Hierarchical decision making problems, such as bilevel programs and Stackelberg games, are attracting increasing interest in both the engineering and machine learning communities.…
Distributed and Constrained Control Design via System Level Synthesis and Dual Consensus ADMM
Panagiotis D. Grontas, Michael W. Fisher, Florian Dörfler
Design of optimal distributed linear feedback controllers to achieve a desired aggregate behavior, while simultaneously satisfying state and input constraints, is a challenging but…