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 1476–1500 of 1963 papers

TitleStatusHype
Monte Carlo inference for semiparametric Bayesian regression—0
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models—0
Monte Carlo Tree Descent for Black-Box Optimization—0
Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments—0
Motor cortex mapping using active gaussian processes—0
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints—0
Multi-Agent Clarity-Aware Dynamic Coverage with Gaussian Processes—0
Multi-Agent Safe Planning with Gaussian Processes—0
Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection—0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Leveraging Probabilistic Circuits for Nonparametric Multi-Output RegressionCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy SearchCode0
Likelihood-Free Inference with Deep Gaussian ProcessesCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Estimation of Dynamic Gaussian ProcessesCode0
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm DesignCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervalsCode0
The Shape of Learning Curves: a ReviewCode0
Physics-informed Gaussian Processes for Safe Envelope ExpansionCode0
Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal dataCode0
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Benchmark Results

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