"gaussian processes" Papers

Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation

Da Long, Zhitong Xu, Guang Yang et al.

Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations

David Dalton, Alan Lazarus, Hao Gao et al.

Deep Random Features for Scalable Interpolation of Spatiotemporal Data

Weibin Chen, Azhir Mahmood, Michel Tsamados et al.

Fairness-aware Bayes Optimal Functional Classification

Xiaoyu Hu, Gengyu Xue, Zhenhua Lin et al.

Gaussian Processes for Shuffled Regression

Masahiro Kohjima

Identifiability for Gaussian Processes with Holomorphic Kernels

Ameer Qaqish, Didong Li

Infinite Neural Operators: Gaussian processes on functions

Daniel Augusto de Souza, Yuchen Zhu, Jake Cunningham et al.

Informed Initialization for Bayesian Optimization and Active Learning

Carl Hvarfner, David Eriksson, Eytan Bakshy et al.

No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes

Jasmine Bayrooti, Sattar Vakili, Amanda Prorok et al.

Policy Gradient with Kernel Quadrature

Tetsuro Morimura, Satoshi Hayakawa

Quantitative convergence of trained neural networks to Gaussian processes

Andrea Agazzi, Eloy Mosig García, Dario Trevisan

Residual Deep Gaussian Processes on Manifolds

Kacper Wyrwal, Andreas Krause, Viacheslav (Slava) Borovitskiy

Robust and Computation-Aware Gaussian Processes

Solving and Learning Partial Differential Equations with Variational Q-Exponential Processes

Guangting Yu, Shiwei Lan

Amortized Variational Deep Kernel Learning

Alan Matias, César Lincoln Mattos, Joao Paulo Gomes et al.

A Rate-Distortion View of Uncertainty Quantification

Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen et al.

BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition

Shikai Fang, Qingsong Wen, Yingtao Luo et al.

Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing

Lokesh Nagalapatti, Akshay Iyer, Abir De et al.

Critical feature learning in deep neural networks

Kirsten Fischer, Javed Lindner, David Dahmen et al.

Domain Invariant Learning for Gaussian Processes and Bayesian Exploration

Xilong Zhao, Siyuan Bian, Yaoyun Zhang et al.

Gaussian Processes on Cellular Complexes

Mathieu Alain, So Takao, Brooks Paige et al.

Gaussian Process Neural Additive Models

Wei Zhang, Brian Barr, John Paisley

Latent variable model for high-dimensional point process with structured missingness

Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki

Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning

Vivienne Wang, Tinghuai Wang, wenyan yang et al.

Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes

Yingyi Chen, Qinghua Tao, Francesco Tonin et al.

Variational Linearized Laplace Approximation for Bayesian Deep Learning

Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato