no code implementations • 4 Apr 2024 • Yuzhen Qin, Ahmed El-Gazzar, Danielle S. Bassett, Fabio Pasqualetti, Marcel van Gerven
In this paper, we employ a bistable model, where a stable equilibrium and a stable limit cycle coexist, to describe epileptic dynamics.
1 code implementation • 3 Feb 2024 • Karthik Elamvazhuthi, Darshan Gadginmath, Fabio Pasqualetti
We learn to control a dynamical system in reverse such that the terminal state belongs to the target set.
no code implementations • 13 Dec 2023 • Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti
We use a control theoretic perspective by posing the approximation of the reverse process as a trajectory tracking problem.
no code implementations • 9 Dec 2023 • Darshan Gadginmath, Shivanshu Tripathi, Fabio Pasqualetti
This study addresses the challenge of online learning in contexts where agents accumulate disparate data, face resource constraints, and use different local algorithms.
no code implementations • 6 Nov 2023 • Lulu Gong, Fabio Pasqualetti, Thomas Papouin, ShiNung Ching
We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts.
no code implementations • 15 May 2023 • Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti
To address this shortcoming, in this paper we study a class of neural ordinary differential equations that, by design, leave a given manifold invariant, and characterize their properties by leveraging the controllability properties of control affine systems.
no code implementations • 4 May 2023 • Abed AlRahman Al Makdah, Fabio Pasqualetti
We leverage the static form of the controller to derive output-feedback controllers that achieve monotonic output tracking of a constant non-negative reference output.
no code implementations • 31 Mar 2023 • Federico Celi, Giacomo Baggio, Fabio Pasqualetti
Additionally, the paper proposes a closed-form expression for the feedback gain that solves the eigenstructure assignment problem.
no code implementations • 16 Mar 2023 • Taosha Guo, Abed AlRahman Al Makdah, Vishaal Krishnan, Fabio Pasqualetti
In this paper we study an imitation and transfer learning setting for Linear Quadratic Gaussian (LQG) control, where (i) the system dynamics, noise statistics and cost function are unknown and expert data is provided (that is, sequences of optimal inputs and outputs) to learn the LQG controller, and (ii) multiple control tasks are performed for the same system but with different LQG costs.
no code implementations • 2 Feb 2023 • Yuzhen Qin, Yingcong Li, Fabio Pasqualetti, Maryam Fazel, Samet Oymak
The growing interest in complex decision-making and language modeling problems highlights the importance of sample-efficient learning over very long horizons.
no code implementations • 4 Jan 2023 • Yiting Chen, Ana M. Ospina, Fabio Pasqualetti, Emiliano Dall'Anese
This paper presents a system identification framework -- inspired by multi-task learning -- to estimate the dynamics of a given number of linear time-invariant (LTI) systems jointly by leveraging structural similarities across the systems.
no code implementations • 12 May 2022 • Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti
Humans are capable of adjusting to changing environments flexibly and quickly.
no code implementations • 1 Apr 2022 • Yuzhen Qin, Danielle S. Bassett, Fabio Pasqualetti
Cluster synchronization underlies various functions in the brain.
no code implementations • 1 Apr 2022 • Abed AlRahman Al Makdah, Vishaal Krishnan, Vaibhav Katewa, Fabio Pasqualetti
In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control problem from a behavioral perspective.
no code implementations • 13 Jan 2022 • Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti
In this paper, we study representation learning for multi-task decision-making in non-stationary environments.
no code implementations • 10 Jan 2022 • Federico Celi, Fabio Pasqualetti
Studying structural properties of linear dynamical systems through invariant subspaces is one of the key contributions of the geometric approach to system theory.
no code implementations • 8 Nov 2021 • Jaimie Swartz, Federico Celi, Fabio Pasqualetti, Alexandra von Meier
As more distributed energy resources (DERs) are connected to the power grid, it becomes increasingly important to ensure safe and effective coordination between legacy voltage regulation devices and inverter-based DERs.
no code implementations • 6 Apr 2021 • Abed AlRahman Al Makdah, Vaibhav Katewa, Fabio Pasqualetti
We propose metrics to quantify the nominal performance of a classifier with an abstain option and its robustness against adversarial perturbations.
no code implementations • 30 Mar 2021 • Abed AlRahman Al Makdah, Vishaal Krishnan, Fabio Pasqualetti
In this work, we propose a framework to learn feedback control policies with guarantees on closed-loop generalization and adversarial robustness.
no code implementations • 15 Mar 2021 • Pragya Srivastava, Peter J. Mucha, Emily Falk, Fabio Pasqualetti, Danielle S. Bassett
For this purpose, we calculate the exact expression of optimal control energy in terms of layer spectra and the relative alignment between the eigenmodes of the input layer and the deeper target layer.
no code implementations • 8 Dec 2020 • Yuzhen Qin, Tommaso Menara, Danielle S. Bassett, Fabio Pasqualetti
Phase-amplitude coupling (PAC) describes the phenomenon where the power of a high-frequency oscillation evolves with the phase of a low-frequency one.
no code implementations • 18 Jun 2020 • Giacomo Baggio, Fabio Pasqualetti
Then, we leverage this data-based representation to derive closed-form data-driven expressions of minimum-energy controls for a wide range of control horizons.
no code implementations • NeurIPS 2020 • Vishaal Krishnan, Abed AlRahman Al Makdah, Fabio Pasqualetti
In contrast to regularization-based approaches, we formulate the adversarially robust learning problem as one of loss minimization with a Lipschitz constraint, and show that the saddle point of the associated Lagrangian is characterized by a Poisson equation with weighted Laplace operator.
no code implementations • 16 Feb 2020 • Shubhankar P. Patankar, Jason Z. Kim, Fabio Pasqualetti, Danielle S. Bassett
Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well-understood.
no code implementations • L4DC 2020 • Rajasekhar Anguluri, Abed AlRahman Al Makdah, Vaibhav Katewa, Fabio Pasqualetti
This paper proposes a new framework and several results to quantify the performance of data-driven state-feedback controllers for linear systems against targeted perturbations of the training data.
no code implementations • 4 Mar 2019 • Abed AlRahman Al Makdah, Vaibhav Katewa, Fabio Pasqualetti
Despite the widespread use of machine learning algorithms to solve problems of technological, economic, and social relevance, provable guarantees on the performance of these data-driven algorithms are critically lacking, especially when the data originates from unreliable sources and is transmitted over unprotected and easily accessible channels.