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 1526–1550 of 1963 papers

TitleStatusHype
A Bayesian Perspective of Statistical Machine Learning for Big DataCode0
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions—0
Unifying Probabilistic Models for Time-Frequency AnalysisCode0
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Gaussian Process Conditional Density Estimation—0
Scaling Gaussian Process Regression with DerivativesCode0
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization BoundsCode0
Variational Calibration of Computer Models—0
Adversarially Robust Optimization with Gaussian Processes—0
A Gaussian Process perspective on Convolutional Neural Networks—0
Scalable Gaussian Processes on Discrete Domains—0
Data Association with Gaussian Processes—0
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes—0
Non-linear process convolutions for multi-output Gaussian processes—0
A General Framework for Fair Regression—0
Harmonizable mixture kernels with variational Fourier features—0
Deep learning with differential Gaussian process flowsCode0
A Hybrid Approach for Trajectory Control Design—0
Deep convolutional Gaussian processesCode0
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU AccelerationCode0
Orthogonally Decoupled Variational Gaussian ProcessesCode0
Modeling longitudinal data using matrix completionCode0
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities—0
Robustness Guarantees for Bayesian Inference with Gaussian ProcessesCode0
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Benchmark Results

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