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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1876–1900 of 1963 papers

TitleStatusHype
Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-ParametersCode0
Kernels for Vector-Valued Functions: a ReviewCode0
Stable behaviour of infinitely wide deep neural networksCode0
Knot Selection in Sparse Gaussian Processes with a Variational Objective FunctionCode0
Variational Fourier features for Gaussian processesCode0
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic FeaturesCode0
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural NetworksCode0
Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control TasksCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Deep Gaussian Processes with Importance-Weighted Variational InferenceCode0
Bayesian Modeling with Gaussian Processes using the GPstuff ToolboxCode0
Laplace Matching for fast Approximate Inference in Latent Gaussian ModelsCode0
Large Linear Multi-output Gaussian Process LearningCode0
Large-Scale Gaussian Processes via Alternating ProjectionCode0
Bayesian Meta-Learning Through Variational Gaussian ProcessesCode0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Last Layer Marginal Likelihood for Invariance LearningCode0
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical DataCode0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
Scalable Bayesian Optimization Using Deep Neural NetworksCode0
Latent variable model for high-dimensional point process with structured missingnessCode0
Latent Variable Multi-output Gaussian Processes for Hierarchical DatasetsCode0
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
Scalable Bayesian Optimization via Focalized Sparse Gaussian ProcessesCode0
Optimization as Estimation with Gaussian Processes in Bandit SettingsCode0
Show:102550
← PrevPage 76 of 79Next →

Benchmark Results

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