no code implementations • 16 Apr 2024 • Sajjad Maleki, Subhash Lakshminarayana, E. Veronica Belmega, Carsten Maple
Phasor Measurement Units (PMUs) are used in the measurement, control and protection of power grids.
no code implementations • 10 Nov 2023 • Sajjad Maleki, Shijie Pan, E. Veronica Belmega, Charalambos Konstantinou, Subhash Lakshminarayana
Load-altering attacks (LAAs) pose a significant threat to power systems with Internet of Things (IoT)-controllable load devices.
no code implementations • 8 Jun 2023 • Ibrahim Sbeity, Christophe Villien, Benoît Denis, E. Veronica Belmega
In the Global Navigation Satellite System (GNSS) context, the growing number of available satellites has lead to many challenges when it comes to choosing the most accurate pseudorange contributions, given the strong impact of biased measurements on positioning accuracy, particularly in single-epoch scenarios.
no code implementations • ICLR 2021 • Kimon Antonakopoulos, E. Veronica Belmega, Panayotis Mertikopoulos
We present a new family of min-max optimization algorithms that automatically exploit the geometry of the gradient data observed at earlier iterations to perform more informative extra-gradient steps in later ones.
no code implementations • ICLR 2020 • Kimon Antonakopoulos, E. Veronica Belmega, Panayotis Mertikopoulos
Motivated by applications to machine learning and imaging science, we study a class of online and stochastic optimization problems with loss functions that are not Lipschitz continuous; in particular, the loss functions encountered by the optimizer could exhibit gradient singularities or be singular themselves.
no code implementations • 12 Apr 2018 • E. Veronica Belmega, Panayotis Mertikopoulos, Romain Negrel, Luca Sanguinetti
Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical and algorithmic tools of online optimization have found widespread use in problems where the trade-off between data exploration and exploitation plays a predominant role.
no code implementations • 3 Jun 2016 • Panayotis Mertikopoulos, E. Veronica Belmega, Romain Negrel, Luca Sanguinetti
In this paper, we investigate a distributed learning scheme for a broad class of stochastic optimization problems and games that arise in signal processing and wireless communications.