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 301–350 of 1963 papers

TitleStatusHype
Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting—0
Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference—0
Gradient-enhanced deep Gaussian processes for multifidelity modelling—0
Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting—0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes LearningCode0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Nowcasting with Mixed Frequency Data Using Gaussian Processes—0
Resilience of Rademacher chaos of low degree—0
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes—0
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Neural Networks Asymptotic Behaviours for the Resolution of Inverse Problems—0
Trained quantum neural networks are Gaussian processes—0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
A Novel Gaussian Min-Max Theorem and its Applications—0
Safe Active Learning for Time-Series Modeling with Gaussian Processes—0
Principled Preferential Bayesian OptimizationCode0
Latent variable model for high-dimensional point process with structured missingnessCode0
Gaussian Process-Based Nonlinear Moving Horizon Estimation—0
Voronoi Candidates for Bayesian OptimizationCode0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian OptimizationCode1
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
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian ProcessesCode1
Quantum-Assisted Hilbert-Space Gaussian Process RegressionCode0
Bayesian Causal Inference with Gaussian Process NetworksCode0
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional DataCode0
Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors—0
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
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
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
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Benchmark Results

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