:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chang, Chun-Yi, Sung, Chih-Li
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2508.16434
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Active Learning of Piecewise Gaussian Process Surrogates
by: Park, Chiwoo, et al.
Published: (2023)

Multi-Output Robust and Conjugate Gaussian Processes
by: Rooijakkers, Joshua, et al.
Published: (2025)

Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
by: Vapsi, Annita, et al.
Published: (2026)

Active Learning for Manifold Gaussian Process Regression
by: Cheng, Yuanxing, et al.
Published: (2025)

Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes
by: Chen, Yang, et al.
Published: (2024)

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

Transformed Latent Variable Multi-Output Gaussian Processes
by: Jiang, Xiaoyu, et al.
Published: (2026)

Derivation of Output Correlation Inferences for Multi-Output (aka Multi-Task) Gaussian Process
by: Watanabe, Shuhei
Published: (2025)

Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
by: Jiang, Xiaoyu, et al.
Published: (2024)

Consensus-based Recursive Multi-Output Gaussian Process
by: Rao, Yogesh Prasanna Kumar, et al.
Published: (2026)

Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning
by: Kusne, A. Gilad, et al.
Published: (2023)

Low-rank computation of the posterior mean in Multi-Output Gaussian Processes
by: Esche, Sebastian, et al.
Published: (2025)

Active Learning with Weak Supervision for Gaussian Processes
by: Olmin, Amanda, et al.
Published: (2022)

Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
by: Niu, Ruijia, et al.
Published: (2024)

Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
by: Jaber, Edgar, et al.
Published: (2024)

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
by: Ewering, Jan-Hendrik, et al.
Published: (2025)

Distributionally Robust Active Learning for Gaussian Process Regression
by: Takeno, Shion, et al.
Published: (2025)

Intrinsic Gaussian Process Regression Modeling for Manifold-valued Response Variable
by: Wang, Zhanfeng, et al.
Published: (2024)

Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry
by: Ye, Ke, et al.
Published: (2020)

Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
by: Cutforth, Murray, et al.
Published: (2025)

Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes
by: Zhao, Hao, et al.
Published: (2025)

Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning
by: Chiang, Chia-Cheng, et al.
Published: (2024)

Batch Active Learning in Gaussian Process Regression using Derivatives
by: Yu, Hon Sum Alec, et al.
Published: (2024)

Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
by: Tebbe, Jörn, et al.
Published: (2024)

Safe Active Learning for Time-Series Modeling with Gaussian Processes
by: Zimmer, Christoph, et al.
Published: (2024)

Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions
by: Xu, Yang, et al.
Published: (2025)

Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations
by: Bajracharya, Pradeep, et al.
Published: (2024)

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
by: Tan, Yu, et al.
Published: (2025)

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

Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
by: Polson, Nick, et al.
Published: (2026)

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model
by: Yuan, Mingqi, et al.
Published: (2025)

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
by: Kim, Hwanwoo, et al.
Published: (2024)

An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets
by: Lu, Po-Yi, et al.
Published: (2023)

Multi-Output Distributional Fairness via Post-Processing
by: Li, Gang, et al.
Published: (2024)

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
by: Belakaria, Syrine, et al.
Published: (2024)

Deep Gaussian Processes for Functional Maps
by: Lowery, Matthew, et al.
Published: (2025)

Evaluating Uncertainty in Deep Gaussian Processes
by: van der Lende, Matthijs, et al.
Published: (2025)

Residual Deep Gaussian Processes on Manifolds
by: Wyrwal, Kacper, et al.
Published: (2024)

Intrinsic Vicarious Conditioning for Deep Reinforcement Learning
by: Sanchez, Rodney A, et al.
Published: (2026)

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
by: Zhang, Ruiqi, et al.
Published: (2025)