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 1701–1725 of 1963 papers

TitleStatusHype
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential EquationsCode0
Bayesian Optimization with Tree-structured Dependencies—0
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Robust Bayesian Optimization with Student-t Likelihood—0
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks—0
Location Dependent Dirichlet Processes—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
Variational Bayesian Multiple Instance Learning With Gaussian ProcessesCode0
Statistical abstraction for multi-scale spatio-temporal systems—0
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation—0
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive ControlCode0
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierCode0
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data—0
Large Linear Multi-output Gaussian Process LearningCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Doubly Stochastic Variational Inference for Deep Gaussian ProcessesCode0
Learning of Gaussian Processes in Distributed and Communication Limited Systems—0
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering—0
Entropic Trace Estimates for Log DeterminantsCode0
Asynchronous Distributed Variational Gaussian Processes for Regression—0
Time Series Prediction for Graphs in Kernel and Dissimilarity SpacesCode0
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Benchmark Results

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