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 341–350 of 1963 papers

TitleStatusHype
Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian ProcessesCode0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
Nonlinear bayesian tomography of ion temperature and velocity for Doppler coherence imaging spectroscopy in RT-1—0
Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervalsCode0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
Data-Driven Approaches for Modelling Target Behaviour—0
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure—0
Calibrated Computation-Aware Gaussian ProcessesCode0
Online scalable Gaussian processes with conformal prediction for guaranteed coverage—0
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
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Benchmark Results

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