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Found 12 papers, 5 papers with code
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data.
Data-Driven Observability Analysis for Nonlinear Stochastic Systems
Establishing these properties is challenging, especially when no analytical model is available and they are to be inferred directly from measurement data.
Event-Triggered Time-Varying Bayesian Optimization
Current approaches to TVBO require prior knowledge of a constant rate of change.
Improving the Performance of Robust Control through Event-Triggered Learning
However, in practice, many systems also exhibit uncertainty in the form of changes over time, e. g., due to weight shifts or wear and tear, leading to decreased performance or instability of the learning-based controller.
Parameter Filter-based Event-triggered Learning
However, they usually come with a critical assumption - access to an accurate model of the system.
Safe Value Functions
Although it is often not possible to compute the minimum required penalty, we reveal clear structure of how the penalty, rewards, discount factor, and dynamics interact.
Structure-preserving Gaussian Process Dynamics
On the other hand, classical numerical integrators are specifically designed to preserve these crucial properties through time.
Identifying Causal Structure in Dynamical Systems
In this paper, we propose a method that identifies the causal structure of control systems.
A Kernel Two-sample Test for Dynamical Systems
Evaluating whether data streams are drawn from the same distribution is at the heart of various machine learning problems.
Actively Learning Gaussian Process Dynamics
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way.
A Local Information Criterion for Dynamical Systems
Apart from its application for encoding a sequence of observations, we propose to use the compression achieved by this encoding as a criterion for model selection.
Event-triggered Learning for Resource-efficient Networked Control
Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly.
