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 301–325 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
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes LearningCode0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Resilience of Rademacher chaos of low degree—0
Nowcasting with Mixed Frequency Data Using Gaussian Processes—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
Latent variable model for high-dimensional point process with structured missingnessCode0
Principled Preferential Bayesian OptimizationCode0
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
Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian OptimizationCode1
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
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Benchmark Results

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