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Gaussian Processes

568 directly classified papers

Papers per year

Papers

Infinite Gaussian Mixture Modeling with an Improved Estimation of the Number of Clusters AAAI 2021
Learning Nonparametric Volterra Kernels with Gaussian Processes NIPS 2021
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation NIPS 2021
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations NIPS 2021
Gaussian Process-Based Real-Time Learning for Safety Critical Applications ICML 2021
Spatio-Temporal Variational Gaussian Processes NIPS 2021
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning NIPS 2021
Exploiting a Zoo of Checkpoints for Unseen Tasks NIPS 2021
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes ICML 2021
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems AAAI 2021
Deconditional Downscaling with Gaussian Processes NIPS 2021
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence NIPS 2021
Inference of Black Hole Fluid-Dynamics From Sparse Interferometric Measurements ICCV 2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels NIPS 2021
Raven's Progressive Matrices Completion with Latent Gaussian Process Priors AAAI 2021
Structure-Aware Random Fourier Kernel for Graphs NIPS 2021
Bayesian Optimization with High-Dimensional Outputs NIPS 2021
Multi-Speaker Text-to-Speech Synthesis Using Deep Gaussian Processes INTERSPEECH 2020
Bayesian Optimization using Pseudo-Points IJCAI 2020
Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract) IJCAI 2020
GaSPing for Utility AAAI 2020
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances ICML 2020
Deep Gaussian Markov Random Fields ICML 2020
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification ACL 2020
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations ICML 2020