Cover Image

Comparison of Data Reduction Criteria for Online Gaussian Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wietzke, Thore, Graichen, Knut
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Occupancy Prediction for Building Energy Systems with Latent Force Models
by: Wietzke, Thore, et al.
Published: (2024)

Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows
by: Rabenstein, Georg, et al.
Published: (2024)

Transfer Learning Study of Motion Transformer-based Trajectory Predictions
by: Ullrich, Lars, et al.
Published: (2024)

Dynamic Range Reduction via Branch-and-Bound
by: Gerlach, Thore, et al.
Published: (2024)

Recursive Feasibility of Nonlinear Stochastic Model Predictive Control With Gaussian Process Dynamics
by: Daniel Landgraf, et al.
Published: (2026)

Recurrent Memory for Online Interdomain Gaussian Processes
by: Chen, Wenlong, et al.
Published: (2025)

Fast Gaussian Process Approximations for Autocorrelated Data
by: Chokhachian, Ahmadreza, et al.
Published: (2025)

On the Usage of Gaussian Process for Efficient Data Valuation
by: Bénesse, Clément, et al.
Published: (2025)

GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
by: Dai, Xiaobing, et al.
Published: (2024)

Robust, Online, and Adaptive Decentralized Gaussian Processes
by: Llorente, Fernando, et al.
Published: (2025)

Multi-Criteria Comparison as a Method of Advancing Knowledge-Guided Machine Learning
by: Harman, Jason L., et al.
Published: (2024)

Multi-Output Gaussian Processes for Graph-Structured Data
by: Nakai-Kasai, Ayano, et al.
Published: (2025)

Small Loss Bounds for Online Learning Separated Function Classes: A Gaussian Process Perspective
by: Block, Adam, et al.
Published: (2025)

Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks
by: Alipourfard, Borzou, et al.
Published: (2018)

Nowcasting with Mixed Frequency Data Using Gaussian Processes
by: Hauzenberger, Niko, et al.
Published: (2024)

DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process
by: Abu-Shaira, Mohammad, et al.
Published: (2025)

Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version
by: Yang, Zewen, et al.
Published: (2025)

Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems
by: Llorente, Fernando, et al.
Published: (2025)

Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning
by: Gerlach, Thore, et al.
Published: (2025)

Kernel $k$-Medoids as General Vector Quantization
by: Gerlach, Thore, et al.
Published: (2025)

Variational Gaussian Process Diffusion Processes
by: Verma, Prakhar, et al.
Published: (2023)

Empirical Gaussian Processes
by: Lin, Jihao Andreas, et al.
Published: (2026)

Robust Meta-Learning of Vehicle Yaw Rate Dynamics via Conditional Neural Processes
by: Ullrich, Lars, et al.
Published: (2024)

Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation
by: Nadew, Yididiya Y., et al.
Published: (2024)

An Explainable Gaussian Process Auto-encoder for Tabular Data
by: Zhang, Wei, et al.
Published: (2025)

Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
by: Hsin, Junette, et al.
Published: (2023)

Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document
by: Yang, Zewen, et al.
Published: (2024)

Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood
by: Shikuri, Yuta
Published: (2024)

Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process
by: Ravi, Sandipp Krishnan, et al.
Published: (2024)

Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Data
by: Li, Haoyu, et al.
Published: (2025)

Sparse Gaussian Neural Processes
by: Rochussen, Tommy, et al.
Published: (2025)

Matérn Gaussian Processes on Graphs
by: Borovitskiy, Viacheslav, et al.
Published: (2020)

Operator Learning with Gaussian Processes
by: Mora, Carlos, et al.
Published: (2024)

Gaussian Processes on Cellular Complexes
by: Alain, Mathieu, et al.
Published: (2023)

Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference
by: Lin, Zhidi, et al.
Published: (2023)

Data-Driven Stochastic AC-OPF using Gaussian Processes
by: Mitrovic, Mile
Published: (2024)

A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics
by: Gosnell, Arron, et al.
Published: (2024)

Sketching the Heat Kernel: Using Gaussian Processes to Embed Data
by: Gilbert, Anna C., et al.
Published: (2024)

MAGPI: Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
by: Rex, Atticus, et al.
Published: (2026)

Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoods
by: Seitz, Sarem
Published: (2022)