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 1281–1290 of 1963 papers

TitleStatusHype
Deep Reinforcement Learning with Weighted Q-Learning—0
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes—0
Linear-time inference for Gaussian Processes on one dimension—0
Amortized variance reduction for doubly stochastic objectives—0
Modelling Human Active Search in Optimizing Black-box Functions—0
Scalable Uncertainty for Computer Vision with Functional Variational Inference—0
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations—0
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with DerivativesCode0
Knot Selection in Sparse Gaussian Processes with a Variational Objective FunctionCode0
Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns—0
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Benchmark Results

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