Image de couverture de livre

Polynomial Chaos Expanded Gaussian Process

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Polke, Dominik, Kösters, Tim, Ahle, Elmar, Söffker, Dirk
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Systems and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Advanced POD-Based Performance Evaluation of Classifiers Applied to Human Driver Lane Changing Prediction
par: Rastin, Zahra, et autres
Publié: (2024)

Reliability comparison of vessel trajectory prediction models via Probability of Detection
par: Rastin, Zahra, et autres
Publié: (2025)

Learning battery model parameter dynamics from data with recursive Gaussian process regression
par: Aitio, Antti, et autres
Publié: (2023)

Koopman-Equivariant Gaussian Processes
par: Bevanda, Petar, et autres
Publié: (2025)

Recursive Gaussian Process State Space Model
par: Zheng, Tengjie, et autres
Publié: (2024)

Stability Enhanced Gaussian Process Variational Autoencoders
par: Richardson, Carl R., et autres
Publié: (2026)

Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
par: Hsin, Junette, et autres
Publié: (2023)

Physics-informed Gaussian Processes as Linear Model Predictive Controller
par: Tebbe, Jörn, et autres
Publié: (2024)

A Lane Change Assistance System Based on Prediction of Driver Intention
par: Tanshi, Foghor, et autres
Publié: (2024)

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

Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty
par: Faulwasser, T., et autres
Publié: (2023)

Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
par: Lederer, Armin, et autres
Publié: (2019)

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
par: Ewering, Jan-Hendrik, et autres
Publié: (2025)

Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
par: Glover, Daniel, et autres
Publié: (2025)

A hybrid prognosis approach for robust lifetime control of commercial wind turbines
par: Kipchirchir, Edwin, et autres
Publié: (2024)

Learning Passive Continuous-Time Dynamics with Multistep Port-Hamiltonian Gaussian Processes
par: Leung, Chi Ho, et autres
Publié: (2025)

Data-Driven Prediction and Control of Hammerstein-Wiener Systems with Implicit Gaussian Processes
par: Yin, Mingzhou, et autres
Publié: (2025)

Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version
par: Yang, Zewen, et autres
Publié: (2025)

Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document
par: Yang, Zewen, et autres
Publié: (2024)

Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior
par: Xinming, Wang, et autres
Publié: (2024)

Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
par: Zhang, Yu, et autres
Publié: (2024)

Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach
par: Patnaik, Ayush, et autres
Publié: (2025)

Quasi-Periodic Gaussian Process Predictive Iterative Learning Control
par: Nigam, Unnati, et autres
Publié: (2026)

Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies
par: Yang, Zewen, et autres
Publié: (2024)

Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression
par: Dai, Xiaobing, et autres
Publié: (2024)

A Tutorial on Gaussian Process Learning-based Model Predictive Control
par: Wang, Jie, et autres
Publié: (2024)

Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach
par: Donandt, Kathrin, et autres
Publié: (2024)

Beyond expected value: geometric mean optimization for long-term policy performance in reinforcement learning
par: Sheng, Xinyi, et autres
Publié: (2025)

Logarithmic Regret and Polynomial Scaling in Online Multi-step-ahead Prediction
par: Qian, Jiachen, et autres
Publié: (2025)

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

Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function -- Part I: Theoretical Framework and Off-line Study
par: Zhai, Chao, et autres
Publié: (2019)

HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting
par: Song, Pengyang, et autres
Publié: (2025)

Modeling Epidemic Spread: A Gaussian Process Regression Approach
par: She, Baike, et autres
Publié: (2023)

Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference
par: Lefringhausen, Robert, et autres
Publié: (2025)

Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes
par: Tesfazgi, Samuel, et autres
Publié: (2024)

PACSBO: Probably approximately correct safe Bayesian optimization
par: Tokmak, Abdullah, et autres
Publié: (2024)

Towards safe control parameter tuning in distributed multi-agent systems
par: Tokmak, Abdullah, et autres
Publié: (2025)

Safe Bayesian optimization across noise models via scenario programming
par: Tokmak, Abdullah, et autres
Publié: (2025)

Bayesian Optimization of Process Parameters of a Sensor-Based Sorting System using Gaussian Processes as Surrogate Models
par: Kronenwett, Felix, et autres
Publié: (2025)

Sliced Wasserstein Steering between Gaussian Measures
par: Ito, Kaito, et autres
Publié: (2026)