Image de couverture de livre

Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Veedu, Mishfad Shaikh, Deka, Deepjyoti, Salapaka, Murti V.
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning Systems and Control Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Topology Learning of unknown Networked Linear Dynamical System excited by Cyclostationary inputs
par: Doddi, Harish, et autres
Publié: (2020)

Uncovering the Influence Flow Model of Transistor Amplifiers, Its Reconstruction and Application
par: Rana, Mohammed Tuhin, et autres
Publié: (2025)

Distributed Difference of Convex Optimization
par: Khatana, Vivek, et autres
Publié: (2024)

Function Gradient Approximation with Random Shallow ReLU Networks with Control Applications
par: Lamperski, Andrew, et autres
Publié: (2024)

Watts vs. Bytes: Turning Data Centers into Grid Assets via Storage Compute Co-Optimization
par: Liu, Shaohui, et autres
Publié: (2026)

PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels
par: Li, Wenting, et autres
Publié: (2021)

Mitigating the Impact of Uncertain Wildfire Risk on Power Grids through Topology Control
par: Zhou, Yuqi, et autres
Publié: (2023)

Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens
par: Moghaddam, Amirreza Neshaei, et autres
Publié: (2024)

The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
par: Muehlebach, Michael, et autres
Publié: (2025)

Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity
par: Boveiri, Mohammad, et autres
Publié: (2024)

Sample Complexity of Linear Quadratic Regulator Without Initial Stability
par: Moghaddam, Amirreza Neshaei, et autres
Publié: (2025)

Learning Optimal Control and Dynamical Structure of Global Trajectory Search Problems with Diffusion Models
par: Graebner, Jannik, et autres
Publié: (2024)

The value of storage in electricity distribution: The role of markets
par: Lauinger, Dirk, et autres
Publié: (2025)

Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems
par: Kuang, Simon, et autres
Publié: (2023)

Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
par: Pareek, Parikshit, et autres
Publié: (2023)

Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
par: Sattar, Yahya, et autres
Publié: (2021)

A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
par: Chakrabarti, Kushal, et autres
Publié: (2022)

Finite Sample Identification of Partially Observed Bilinear Dynamical Systems
par: Sattar, Yahya, et autres
Publié: (2025)

Mutual Information Optimal Control of Discrete-Time Linear Systems
par: Enami, Shoju, et autres
Publié: (2025)

On Policy Stochasticity in Mutual Information Optimal Control of Linear Systems
par: Enami, Shoju, et autres
Publié: (2025)

Safe and Near-Optimal Control with Online Dynamics Learning
par: Prajapat, Manish, et autres
Publié: (2025)

Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
par: Prajapat, Manish, et autres
Publié: (2025)

Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates
par: Maity, Sreejeet, et autres
Publié: (2025)

Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
par: Ding, Jianglin, et autres
Publié: (2025)

Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
par: Authier, Jules, et autres
Publié: (2023)

End-to-End Learning Framework for Solving Non-Markovian Optimal Control
par: Zhang, Xiaole, et autres
Publié: (2025)

Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
par: Pineda, Salvador, et autres
Publié: (2024)

Mutual Information Optimal Density Control of Linear Systems and Generalized Schrödinger Bridges with Reference Refinement
par: Enami, Shoju, et autres
Publié: (2026)

Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies
par: Pareek, Parikshit, et autres
Publié: (2023)

Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
par: Lefringhausen, Robert, et autres
Publié: (2023)

Learning Exactly Linearizable Deep Dynamics Models
par: Moriyasu, Ryuta, et autres
Publié: (2023)

Learning Linear Dynamics from Bilinear Observations
par: Sattar, Yahya, et autres
Publié: (2024)

Towards Enabling Learning for Time-Varying finite horizon Sequential Decision-Making Problems*
par: Tiwari, Dhananjay, et autres
Publié: (2025)

Safely Learning Dynamical Systems
par: Ahmadi, Amir Ali, et autres
Publié: (2023)

Finite Sample Frequency Domain Identification
par: Tsiamis, Anastasios, et autres
Publié: (2024)

The Ground Cost for Optimal Transport of Angular Velocity
par: Elamvazhuthi, Karthik, et autres
Publié: (2025)

Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
par: Zhou, Quan, et autres
Publié: (2020)

Learning Dissipative Neural Dynamical Systems
par: Xu, Yuezhu, et autres
Publié: (2023)

Deterministic Trajectory Optimization through Probabilistic Optimal Control
par: Filabadi, Mohammad Mahmoudi, et autres
Publié: (2024)

Towards an Optimal Control Perspective of ResNet Training
par: Püttschneider, Jens, et autres
Publié: (2025)