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 701–710 of 1963 papers

TitleStatusHype
Extrinsic Bayesian Optimizations on Manifolds—0
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processesCode0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact SupervisionCode0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Information-Theoretic Safe Exploration with Gaussian ProcessesCode0
Deep Kernel Learning for Mortality Prediction in the Face of Temporal ShiftCode0
Gaussian Process Barrier States for Safe Trajectory Optimization and Control—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
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Benchmark Results

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