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 51–75 of 1963 papers

TitleStatusHype
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows—0
Sparse Gaussian Neural ProcessesCode0
Preconditioned Additive Gaussian Processes with Fourier Acceleration—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes—0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization—0
Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters—0
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functionsCode0
Large Scale Multi-Task Bayesian Optimization with Large Language Models—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian ProcessCode2
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?—0
A physics-informed Bayesian optimization method for rapid development of electrical machines—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation—0
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative Model for Quasar SpectraCode0
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood—0
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Provable Quantum Algorithm Advantage for Gaussian Process QuadratureCode0
Robust Optimization with Diffusion Models for Green Security—0
Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control—0
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Benchmark Results

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