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 1551–1575 of 1963 papers

TitleStatusHype
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model CalibrationCode0
Machine Learning of Linear Differential Equations using Gaussian ProcessesCode0
MAGMA: Inference and Prediction with Multi-Task Gaussian ProcessesCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Federated Causal Inference from Observational DataCode0
Manifold Gaussian Processes for RegressionCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate dataCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Marginalised Gaussian Processes with Nested SamplingCode0
Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning ApproachCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average ConstraintsCode0
Principled Preferential Bayesian OptimizationCode0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
Batch Bayesian Optimization via Local PenalizationCode0
Privacy Preserving Federated Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation DataCode0
Deep Bayesian Optimization on Attributed GraphsCode0
Streaming Variational Monte CarloCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
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Benchmark Results

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