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 951–975 of 1963 papers

TitleStatusHype
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
Input Dependent Sparse Gaussian Processes—0
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging—0
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Interpretable deep Gaussian processes with moments—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Interrelation of equivariant Gaussian processes and convolutional neural networks—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Gaussian Process Latent Class Choice Models—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Introduction and Exemplars of Uncertainty Decomposition—0
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Attentive Gaussian processes for probabilistic time-series generation—0
Is SGD a Bayesian sampler? Well, almost—0
A General Framework for Fair Regression—0
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Benchmark Results

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