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 1551–1575 of 1963 papers

TitleStatusHype
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Assessing Quality Estimation Models for Sentence-Level Prediction—0
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes—0
Remote sensing image regression for heterogeneous change detection—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces—0
On Lebesgue Integral Quadrature—0
Mixed-Stationary Gaussian Process for Flexible Non-Stationary Modeling of Spatial Outcomes—0
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient MatchingCode0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)Code1
Conditional Neural ProcessesCode1
Adversarial Robustness Toolbox v1.0.0Code3
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)—0
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm DesignCode0
Probabilistic Bisection with Spatial Metamodels—0
Bayesian Deep Learning on a Quantum ComputerCode0
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees—0
Neural-net-induced Gaussian process regression for function approximation and PDE solution—0
Stagewise Safe Bayesian Optimization with Gaussian Processes—0
Neural Tangent Kernel: Convergence and Generalization in Neural NetworksCode1
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte CarloCode0
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Benchmark Results

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