SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1151–1175 of 1963 papers

TitleStatusHype
Bayesian Variational Optimization for Combinatorial Spaces—0
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process—0
Sample-efficient reinforcement learning using deep Gaussian processes—0
Learning in the Wild with Incremental Skeptical Gaussian ProcessesCode0
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian ProcessesCode0
Inter-domain Deep Gaussian Processes—0
Marginalised Gaussian Processes with Nested SamplingCode0
Matérn Gaussian Processes on Graphs—0
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization—0
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
Local Granger Causality—0
Statistical Analysis of the LMS Algorithm for Proper and Improper Gaussian Processes—0
Deep Importance Sampling based on Regression for Model Inversion and Emulation—0
Semi-parametric γ-ray modeling with Gaussian processes and variational inferenceCode0
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural NetworksCode0
Graph Based Gaussian Processes on Restricted Domains—0
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes—0
Few-shot Learning for Spatial Regression—0
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning—0
Splitting Gaussian Process Regression for Streaming Data—0
Gene Regulatory Network Inference with Latent Force Models—0
Detecting Misclassification Errors in Neural Networks with a Gaussian Process ModelCode0
Deep kernel processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified