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 1951–1963 of 1963 papers

TitleStatusHype
Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
Large Scale Multi-Task Bayesian Optimization with Large Language Models—0
Large-width functional asymptotics for deep Gaussian neural networks—0
Latent Map Gaussian Processes for Mixed Variable Metamodeling—0
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data—0
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control—0
LazyPPL: laziness and types in non-parametric probabilistic programs—0
Learning about a changing state—0
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy—0
Learning-based decentralized control with collision avoidance for multi-agent systems—0
Learning-Based Fault-Tolerant Control for an Hexarotor with Model Uncertainty—0
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Benchmark Results

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