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 451–500 of 1963 papers

TitleStatusHype
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales—0
Tensor Network-Constrained Kernel Machines as Gaussian ProcessesCode0
Multi-Agent Clarity-Aware Dynamic Coverage with Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
Learning Piecewise Residuals of Control Barrier Functions for Safety of Switching Systems using Multi-Output Gaussian Processes—0
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin—0
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy SearchCode0
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detectionCode0
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression—0
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
Function-space Parameterization of Neural Networks for Sequential LearningCode0
Is Data All That Matters? The Role of Control Frequency for Learning-Based Sampled-Data Control of Uncertain SystemsCode0
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes—0
Learning High-Order Control Barrier Functions for Safety-Critical Control with Gaussian Processes—0
Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints—0
PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates—0
Explainable Learning with Gaussian ProcessesCode0
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Deep Horseshoe Gaussian Processes—0
Robustness bounds on the successful adversarial examples in probabilistic models: Implications from Gaussian processes—0
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling—0
Mixed Strategy Nash Equilibrium for Crowd Navigation—0
Sketching the Heat Kernel: Using Gaussian Processes to Embed Data—0
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models—0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
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
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
Voronoi Candidates for Bayesian OptimizationCode0
Gaussian Process-Based Nonlinear Moving Horizon Estimation—0
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
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Benchmark Results

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