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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 1851–1875 of 1963 papers

TitleStatusHype
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
Necessary and Sufficient Conditions for Surrogate Functions of Pareto Frontiers and Their Synthesis Using Gaussian Processes—0
Spike and Slab Gaussian Process Latent Variable Models—0
Indian Buffet process for model selection in convolved multiple-output Gaussian processesCode0
Differentiating the multipoint Expected Improvement for optimal batch design—0
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical DataCode0
Scalable Bayesian Optimization Using Deep Neural NetworksCode0
Proper Complex Gaussian Processes for Regression—0
Gaussian Processes for Data-Efficient Learning in Robotics and ControlCode0
Distributed Gaussian Processes—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Regression with Linear Factored Functions—0
Nested Variational Compression in Deep Gaussian Processes—0
Extended and Unscented Gaussian Processes—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Probabilistic Differential Dynamic Programming—0
Two Gaussian Approaches to Black-Box Optomization—0
A chain rule for the expected suprema of Gaussian processes—0
Entropy of Overcomplete Kernel Dictionaries—0
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes—0
Graphical LASSO Based Model Selection for Time Series—0
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Benchmark Results

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