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Found 26 papers, 1 papers with code
Analytical Characterization of Epileptic Dynamics in a Bistable System
In this paper, we employ a bistable model, where a stable equilibrium and a stable limit cycle coexist, to describe epileptic dynamics.
Denoising Diffusion-Based Control of Nonlinear Systems
We learn to control a dynamical system in reverse such that the terminal state belongs to the target set.
Noise in the reverse process improves the approximation capabilities of diffusion models
We use a control theoretic perspective by posing the approximation of the reverse process as a trajectory tracking problem.
Fusing Multiple Algorithms for Heterogeneous Online Learning
This study addresses the challenge of online learning in contexts where agents accumulate disparate data, face resource constraints, and use different local algorithms.
Astrocytes as a mechanism for meta-plasticity and contextually-guided network function
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.
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs
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.
Model-based and Data-based Dynamic Output Feedback for Externally Positive Systems
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.
Data-driven Eigenstructure Assignment for Sparse Feedback Design
Additionally, the paper proposes a closed-form expression for the feedback gain that solves the eigenstructure assignment problem.
Imitation and Transfer Learning for LQG Control
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.
Stochastic Contextual Bandits with Long Horizon Rewards
The growing interest in complex decision-making and language modeling problems highlights the importance of sample-efficient learning over very long horizons.
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems
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.
Representation Learning for Context-Dependent Decision-Making
Humans are capable of adjusting to changing environments flexibly and quickly.
Vibrational Control of Cluster Synchronization: Connections with Deep Brain Stimulation
Cluster synchronization underlies various functions in the brain.
Behavioral Feedback for Optimal LQG Control
In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control problem from a behavioral perspective.
Non-Stationary Representation Learning in Sequential Linear Bandits
In this paper, we study representation learning for multi-task decision-making in non-stationary environments.
Data-driven Meets Geometric Control: Zero Dynamics, Subspace Stabilization, and Malicious Attacks
Studying structural properties of linear dynamical systems through invariant subspaces is one of the key contributions of the geometric approach to system theory.
Parameter Conditions to Prevent Voltage Oscillations Caused by LTC-Inverter Hunting on Power Distribution Grids
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.
Robust Adversarial Classification via Abstaining
We propose metrics to quantify the nominal performance of a classifier with an abstain option and its robustness against adversarial perturbations.
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness
In this work, we propose a framework to learn feedback control policies with guarantees on closed-loop generalization and adversarial robustness.
Structural underpinnings of control in multiplex networks
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.
Phase-Amplitude Coupling in Neuronal Oscillator Networks
Phase-amplitude coupling (PAC) describes the phenomenon where the power of a high-frequency oscillation evolves with the phase of a low-frequency one.
Learning Minimum-Energy Controls from Heterogeneous Data
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.
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
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.
Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks
Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well-understood.
On the Robustness of Data-Driven Controllers for Linear Systems
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.
A Fundamental Performance Limitation for Adversarial Classification
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.
