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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1551–1600 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
Deconditional Downscaling with Gaussian ProcessesCode0
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance LearningCode0
Stream-level flow matching with Gaussian processesCode0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and PredictionCode0
A Markov Reward Process-Based Approach to Spatial InterpolationCode0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal TransportCode0
Decomposing Gaussians with Unknown CovarianceCode0
Black-box Coreset Variational InferenceCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Functional Variational Bayesian Neural NetworksCode0
Function-Space Distributions over KernelsCode0
Function-space Parameterization of Neural Networks for Sequential LearningCode0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian OptimizationCode0
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent SystemsCode0
Semi-parametric γ-ray modeling with Gaussian processes and variational inferenceCode0
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Probabilistic Metamodels for an Efficient Characterization of Complex Driving ScenariosCode0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive VarianceCode0
Avoiding pathologies in very deep networksCode0
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Time Series Prediction for Graphs in Kernel and Dissimilarity SpacesCode0
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Benchmark Results

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