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 751–800 of 1963 papers

TitleStatusHype
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture—0
Gaussian Processes on Cellular Complexes—0
Gaussian Processes on Distributions based on Regularized Optimal Transport—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Gaussian Processes on Hypergraphs—0
Gaussian Processes Over Graphs—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
Gaussian Processes with Context-Supported Priors for Active Object Localization—0
Gaussian Processes with Differential Privacy—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation—0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
Gaussian Process for Trajectories—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Attentive Gaussian processes for probabilistic time-series generation—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Gaussian Process Latent Class Choice Models—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Bayesian Warped Gaussian Processes—0
Gaussian Process Neurons—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Gaussian Process on the Product of Directional Manifolds—0
A Perspective on Gaussian Processes for Earth Observation—0
Bayesian Variational Optimization for Combinatorial Spaces—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
A computationally lightweight safe learning algorithm—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Gaussian Process Surrogate Models for Neural Networks—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
Gaussian Process Volatility Model—0
Gauss-Legendre Features for Gaussian Process Regression—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
Efficient Global Optimization using Deep Gaussian Processes—0
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Benchmark Results

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