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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–550 of 1963 papers

TitleStatusHype
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors—0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Quantum-Assisted Hilbert-Space Gaussian Process RegressionCode0
Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors—0
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional DataCode0
Towards Improved Variational Inference for Deep Bayesian Models—0
Sparse discovery of differential equations based on multi-fidelity Gaussian process—0
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes—0
Simulation Based Bayesian OptimizationCode0
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
Learning about a changing state—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological imagesCode0
Time-changed normalizing flows for accurate SDE modeling—0
Sample Path Regularity of Gaussian Processes from the Covariance Kernel—0
Longitudinal prediction of DNA methylation to forecast epigenetic outcomesCode0
Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes—0
Domain Invariant Learning for Gaussian Processes and Bayesian ExplorationCode0
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation—0
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty—0
Sparse Variational Student-t Processes—0
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning—0
Scalable Meta-Learning with Gaussian Processes—0
Estimation of Dynamic Gaussian ProcessesCode0
Gaussian Processes for Monitoring Air-Quality in KampalaCode0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization—0
Variational Elliptical Processes—0
BOIS: Bayesian Optimization of Interconnected Systems—0
Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)—0
Spatial Bayesian Neural NetworksCode0
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options—0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and H_0 inferenceCode0
Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics—0
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes—0
Gaussian Processes on Cellular Complexes—0
Variational Gaussian Processes For Linear Inverse Problems—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Robust and Conjugate Gaussian Process RegressionCode0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Hodge-Compositional Edge Gaussian ProcessesCode0
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Benchmark Results

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