:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Thomas T. C. K., Toso, Leonardo F., Anderson, James, Matni, Nikolai
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Systems and Control
Online Access:	https://arxiv.org/abs/2308.04428
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
by: Lee, Bruce D., et al.
Published: (2024)

Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
by: Zhang, Thomas T., et al.
Published: (2024)

On the Gradient Domination of the LQG Problem
by: Fallah, Kasra, et al.
Published: (2025)

Adversarially Robust Multitask Adaptive Control
by: Fallah, Kasra, et al.
Published: (2025)

Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic Learning
by: Yang, Fengjun, et al.
Published: (2024)

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
by: Zhan, Donglin, et al.
Published: (2025)

Nonasymptotic Regret Analysis of Adaptive Linear Quadratic Control with Model Misspecification
by: Lee, Bruce D., et al.
Published: (2023)

Single Trajectory Conformal Prediction
by: Lee, Brian, et al.
Published: (2024)

A Tutorial on the Non-Asymptotic Theory of System Identification
by: Ziemann, Ingvar, et al.
Published: (2023)

Action Chunking and Exploratory Data Collection Yield Exponential Improvements in Behavior Cloning for Continuous Control
by: Zhang, Thomas T., et al.
Published: (2025)

Learning Stabilizing Policies via an Unstable Subspace Representation
by: Toso, Leonardo F., et al.
Published: (2025)

Physics-informed learning under mixing: How physical knowledge speeds up learning
by: Scampicchio, Anna, et al.
Published: (2025)

State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?
by: Ziemann, Ingvar, et al.
Published: (2024)

Active Learning for Control-Oriented Identification of Nonlinear Systems
by: Lee, Bruce D., et al.
Published: (2024)

Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error
by: Somalwar, Anne, et al.
Published: (2025)

Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR
by: Toso, Leonardo F., et al.
Published: (2024)

Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations
by: Lindemann, Lars, et al.
Published: (2021)

Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach
by: Tan, Jinghong, et al.
Published: (2025)

Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems
by: Zhang, Hanli, et al.
Published: (2024)

Policy Gradient for LQR with Domain Randomization
by: Fujinami, Tesshu, et al.
Published: (2025)

Domain Randomization is Sample Efficient for Linear Quadratic Control
by: Fujinami, Tesshu, et al.
Published: (2025)

Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
by: Gahlawat, Aditya, et al.
Published: (2025)

Learning Invariant Visual Representations for Planning with Joint-Embedding Predictive World Models
by: Toso, Leonardo F., et al.
Published: (2026)

Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
by: Zhou, Quan, et al.
Published: (2020)

Learning Dynamical Systems Encoding Non-Linearity within Space Curvature
by: Fichera, Bernardo, et al.
Published: (2024)

Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space With Non-Stationary Data
by: Zhang, Xiwei, et al.
Published: (2024)

Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part I
by: Tian, Yi, et al.
Published: (2022)

Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
by: Tian, Yi, et al.
Published: (2026)

Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function Identifications
by: Hsieh, Po-Yu, et al.
Published: (2024)

Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence
by: Bonalli, Riccardo, et al.
Published: (2023)

Closed-loop Analysis of ADMM-based Suboptimal Linear Model Predictive Control
by: Srikanthan, Anusha, et al.
Published: (2024)

Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens
by: Moghaddam, Amirreza Neshaei, et al.
Published: (2024)

End-to-End Learning Framework for Solving Non-Markovian Optimal Control
by: Zhang, Xiaole, et al.
Published: (2025)

Momentum Benefits Non-IID Federated Learning Simply and Provably
by: Cheng, Ziheng, et al.
Published: (2023)

Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
by: Zhang, Chenyu, et al.
Published: (2024)

Reinforcement Learning in Non-Markovian Environments
by: Chandak, Siddharth, et al.
Published: (2022)

A Quantitative Framework for Navigating Controller Design Tradeoffs under Computational Constraints
by: Verhoek, Chris, et al.
Published: (2026)

Finite Sample Analysis of Tensor Decomposition for Learning Mixtures of Linear Systems
by: Rui, Maryann, et al.
Published: (2024)

$O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation
by: Chandak, Siddharth
Published: (2025)

Risk-averse Learning with Non-Stationary Distributions
by: Wang, Siyi, et al.
Published: (2024)