Poster "gaussian processes" Papers
19 papers found
Conference
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.
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.
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
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.
Critical feature learning in deep neural networks
Kirsten Fischer, Javed Lindner, David Dahmen et al.
Gaussian Processes on Cellular Complexes
Mathieu Alain, So Takao, Brooks Paige et al.
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