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 926–950 of 1963 papers

TitleStatusHype
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes—0
JUMBO: Scalable Multi-task Bayesian Optimization using Offline DataCode0
Gaussian Processes with Differential Privacy—0
A Markov Reward Process-Based Approach to Spatial InterpolationCode0
Federated Estimation of Causal Effects from Observational DataCode1
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models—0
Deconditional Downscaling with Gaussian ProcessesCode0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
GPy-ABCD: A Configurable Automatic Bayesian Covariance Discovery ImplementationCode1
Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization—0
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
Relative Positional Encoding for Transformers with Linear ComplexityCode1
Probabilistic Robust Linear Quadratic Regulators with Gaussian ProcessesCode0
Priors in Bayesian Deep Learning: A Review—0
Value-at-Risk Optimization with Gaussian Processes—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data—0
Normal Tempered Stable Processes and the Pricing of Energy Derivatives—0
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks—0
Laplace Matching for fast Approximate Inference in Latent Gaussian ModelsCode0
Practical and Rigorous Uncertainty Bounds for Gaussian Process RegressionCode0
Numerical Gaussian process Kalman filtering for spatiotemporal systems—0
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging—0
How Bayesian Should Bayesian Optimisation Be?Code0
MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-ValidationCode1
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Benchmark Results

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