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
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
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
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for HealthcareCode1
Normative Modeling of Neuroimaging Data using Scalable Multi-Task Gaussian Processes—0
Wrapped Gaussian Process Regression on Riemannian Manifolds—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Dirichlet-based Gaussian Processes for Large-scale Calibrated ClassificationCode0
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive VarianceCode0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Variational Learning on Aggregate Outputs with Gaussian ProcessesCode0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
Neural Generative Models for Global Optimization with Gradients—0
Heterogeneous Multi-output Gaussian Process PredictionCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels—0
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Bayesian active learning for choice models with deep Gaussian processes—0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
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Benchmark Results

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