← Bayesian & Probabilistic
Machine Learning › Bayesian & Probabilistic ›

Gaussian Processes

568 directly classified papers

Papers per year

Papers

How Good Are Low-Rank Approximations in Gaussian Process Regression? AAAI 2022
Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network AAAI 2022
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels NIPS 2022
On the inability of Gaussian process regression to optimally learn compositional functions NIPS 2022
Nonparametric Embeddings of Sparse High-Order Interaction Events ICML 2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning ICML 2022
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model AAAI 2022
Variational nearest neighbor Gaussian process ICML 2022
Input Dependent Sparse Gaussian Processes ICML 2022
Adversarial Attacks on Gaussian Process Bandits ICML 2022
Efficient Approximate Inference for Stationary Kernel on Frequency Domain ICML 2022
Additive Gaussian Processes Revisited ICML 2022
Gaussian Process Bandits with Aggregated Feedback AAAI 2022
Scalable Deep Gaussian Markov Random Fields for General Graphs ICML 2022
Revisiting Active Sets for Gaussian Process Decoders NIPS 2022
High-dimensional Additive Gaussian Processes under Monotonicity Constraints NIPS 2022
Kernel Interpolation with Sparse Grids NIPS 2022
Value-at-Risk Optimization with Gaussian Processes ICML 2021
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search ICML 2021
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data ICML 2021
Bayesian Optimization of Function Networks NIPS 2021
Inference of Black Hole Fluid-Dynamics From Sparse Interferometric Measurements ICCV 2021
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes ICML 2021
Lenient Regret and Good-Action Identification in Gaussian Process Bandits ICML 2021
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features NIPS 2021