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 1376–1400 of 1963 papers

TitleStatusHype
Learning Neural Optimal Interpolation Models and Solvers—0
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process—0
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler—0
Learning of Gaussian Processes in Distributed and Communication Limited Systems—0
Learning Piecewise Residuals of Control Barrier Functions for Safety of Switching Systems using Multi-Output Gaussian Processes—0
Learning Rigidity-based Flocking Control with Gaussian Processes—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors—0
Learning spectrograms with convolutional spectral kernels—0
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels—0
Learning Structural Kernels for Natural Language Processing—0
Learning Structures in Earth Observation Data with Gaussian Processes—0
Learning supported Model Predictive Control for Tracking of Periodic References—0
Learning Surrogate Potential Mean Field Games via Gaussian Processes: A Data-Driven Approach to Ill-Posed Inverse Problems—0
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification—0
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
Les Houches Lectures on Deep Learning at Large & Infinite Width—0
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning—0
Linearization Turns Neural Operators into Function-Valued Gaussian Processes—0
Linear Latent Force Models using Gaussian Processes—0
Linearly constrained Gaussian processes—0
Linearly Constrained Gaussian Processes with Boundary Conditions—0
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series—0
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Benchmark Results

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