630 citations · 1.2k across the 74 of their papers we have counts for
23 papers · 1 filter
Koopman Kernel Regression
Petar Bevanda, Max Beier, Armin Lederer +3
Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of intere…
Estimating Motor Symptom Presence and Severity in Parkinson's Disease from Wrist Accelerometer Time Series using ROCKET and InceptionTime
Cedric Donié, Neha Das, Satoshi Endo +1
Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatme…
Sharp Calibrated Gaussian Processes
Alexandre Capone, Geoff Pleiss, Sandra Hirche
While Gaussian processes are a mainstay for various engineering and scientific applications, the uncertainty estimates don't satisfy frequentist guarantees and can be miscalibrated…
Safe Reinforcement Learning via Confidence-Based Filters
Sebastian Curi, Armin Lederer, Sandra Hirche +1
Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems. We develop confidence-based safety filters, a control-theoretic approach fo…
Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes
Giulio Evangelisti, Sandra Hirche
This paper proposes a physically consistent Gaussian Process (GP) enabling the identification of uncertain Lagrangian systems. The function space is tailored according to the energ…
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators
Jan Brüdigam, Martin Schuck, Alexandre Capone +2
Gaussian process regression is increasingly applied for learning unknown dynamical systems. In particular, the implicit quantification of the uncertainty of the learned model makes…
Diffeomorphically Learning Stable Koopman Operators
Petar Bevanda, Max Beier, Sebastian Kerz +3
System representations inspired by the infinite-dimensional Koopman operator (generator) are increasingly considered for predictive modeling. Due to the operator's linearity, a ran…
Learning the Koopman Eigendecomposition: A Diffeomorphic Approach
Petar Bevanda, Johannes Kirmayr, Stefan Sosnowski +1
We present a novel data-driven approach for learning linear representations of a class of stable nonlinear systems using Koopman eigenfunctions. By learning the conjugacy map betwe…
Personalized Rehabilitation Robotics based on Online Learning Control
Samuel Tesfazgi, Armin Lederer, Johannes F. Kunz +2
The use of rehabilitation robotics in clinical applications gains increasing importance, due to therapeutic benefits and the ability to alleviate labor-intensive works. However, th…
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Alexandre Capone, Armin Lederer, Sandra Hirche
Gaussian processes have become a promising tool for various safety-critical settings, since the posterior variance can be used to directly estimate the model error and quantify ris…
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning
Nhan Khanh Le, Yang Liu, Quang Minh Nguyen +4
Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XG…
Inverse Reinforcement Learning: A Control Lyapunov Approach
Samuel Tesfazgi, Armin Lederer, Sandra Hirche
Inferring the intent of an intelligent agent from demonstrations and subsequently predicting its behavior, is a critical task in many collaborative settings. A common approach to s…
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
In application areas where data generation is expensive, Gaussian processes are a preferred supervised learning model due to their high data-efficiency. Particularly in model-based…
Deep Learning based Uncertainty Decomposition for Real-time Control
Neha Das, Jonas Umlauft, Armin Lederer +2
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty…
Anticipating the Long-Term Effect of Online Learning in Control
Alexandre Capone, Sandra Hirche
Control schemes that learn using measurement data collected online are increasingly promising for the control of complex and uncertain systems. However, in most approaches of this…
GP3: A Sampling-based Analysis Framework for Gaussian Processes
Armin Lederer, Markus Kessler, Sandra Hirche
Although machine learning is increasingly applied in control approaches, only few methods guarantee certifiable safety, which is necessary for real world applications. These approa…
Localized active learning of Gaussian process state space models
Alexandre Capone, Jonas Umlauft, Thomas Beckers +2
The performance of learning-based control techniques crucially depends on how effectively the system is explored. While most exploration techniques aim to achieve a globally accura…
Real-Time Regression with Dividing Local Gaussian Processes
Armin Lederer, Alejandro Jose Ordonez Conejo, Korbinian Maier +3
The increased demand for online prediction and the growing availability of large data sets drives the need for computationally efficient models. While exact Gaussian process regres…
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves
Armin Lederer, Jonas Umlauft, Sandra Hirche
The posterior variance of Gaussian processes is a valuable measure of the learning error which is exploited in various applications such as safe reinforcement learning and control…
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
Data-driven models are subject to model errors due to limited and noisy training data. Key to the application of such models in safety-critical domains is the quantification of the…
Mean Square Prediction Error of Misspecified Gaussian Process Models
Thomas Beckers, Jonas Umlauft, Sandra Hirche
Nonparametric modeling approaches show very promising results in the area of system identification and control. A naturally provided model confidence is highly relevant for system-…
A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease
Muriel Lang, Franz M. J. Pfister, Jakob Fröhner +7
The assessment of Parkinson's disease (PD) poses a significant challenge as it is influenced by various factors which lead to a complex and fluctuating symptom manifestation. Thus,…
Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems
Thomas Beckers, Dana Kulić, Sandra Hirche
Perfect tracking control for real-world Euler-Lagrange systems is challenging due to uncertainties in the system model and external disturbances. The magnitude of the tracking erro…