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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

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Showing 151–200 of 1963 papers

TitleStatusHype
Active Bayesian Causal InferenceCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo LabelersCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
Gaussian Processes for Missing Value ImputationCode1
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Gaussian Process Optimization with Adaptive Sketching: Scalable and No RegretCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
GP-GS: Gaussian Processes for Enhanced Gaussian SplattingCode1
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental LearningCode1
Graph Neural Processes for Spatio-Temporal ExtrapolationCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Healing Products of Gaussian ProcessesCode1
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
Implicit Gaussian process representation of vector fields over arbitrary latent manifoldsCode1
On Feature Collapse and Deep Kernel Learning for Single Forward Pass UncertaintyCode1
Kalman meets Bellman: Improving Policy Evaluation through Value TrackingCode1
Kernel Interpolation for Scalable Online Gaussian ProcessesCode1
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciencesCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field dataCode1
Low-Precision Arithmetic for Fast Gaussian ProcessesCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for HealthcareCode1
Model-Based Transfer Learning for Contextual Reinforcement LearningCode1
MOGPTK: The Multi-Output Gaussian Process ToolkitCode1
Multi-class Gaussian Process Classification with Noisy InputsCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-ValidationCode1
Neural-BO: A Black-box Optimization Algorithm using Deep Neural NetworksCode1
Neural Diffusion ProcessesCode1
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
Nonnegative spatial factorizationCode1
Operator Learning with Gaussian ProcessesCode1
Optimizing Hyperparameters with Conformal Quantile RegressionCode1
Dense Gaussian Processes for Few-Shot SegmentationCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
Probabilistic Numeric Convolutional Neural NetworksCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Positional Encoder Graph Neural Networks for Geographic DataCode1
Random Forests for dependent dataCode1
Recyclable Gaussian ProcessesCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Scalable Exact Inference in Multi-Output Gaussian ProcessesCode1
Analytical results for uncertainty propagation through trained machine learning regression models—0
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Benchmark Results

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