On L2-Regularization for Virtual Reference Feedback Tuning

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handl ...

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

Operation of a tokamak device requires the coordinated operation of a multitude of systems. The sequence of operations during a discharge is both too complex and fast for human interaction. A common way of operation is a predefined sequence of operations w ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

Neural machine translation (MT) and text generation have recently reached very high levels of quality. However, both areas share a problem: in order to reach these levels, they require massive amounts of data. When this is not present, they lack generaliza ...

On L2-Regularization for Virtual Reference Feedback Tuning

Chat with Graph Search

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Regularization of polynomial networks for image recognition

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Dynamic pulse scheduling in ASDEX Upgrade: Disruption avoidance and investigation of the H-Mode density limit

Regularization of polynomial networks for image recognition

Universal and adaptive methods for robust stochastic optimization

Data-driven Methods for Control: from Linear to Lifting

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Regularization Techniques for Low-Resource Machine Translation