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 1576–1600 of 1963 papers

TitleStatusHype
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
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
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte CarloCode0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
Grouped Gaussian Processes for Solar Power Prediction—0
Variational Implicit ProcessesCode0
Deep Gaussian Processes with Convolutional Kernels—0
Normative Modeling of Neuroimaging Data using Scalable Multi-Task Gaussian Processes—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Wrapped Gaussian Process Regression on Riemannian Manifolds—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Dirichlet-based Gaussian Processes for Large-scale Calibrated ClassificationCode0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive VarianceCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
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Benchmark Results

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