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 401–410 of 1963 papers

TitleStatusHype
Probabilistic Subgoal Representations for Hierarchical Reinforcement learningCode0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows—0
On the Consistency of Kernel Methods with Dependent Observations—0
On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them—0
Linearization Turns Neural Operators into Function-Valued Gaussian Processes—0
Approximation-Aware Bayesian Optimization—0
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes—0
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems—0
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Benchmark Results

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