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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 251–300 of 1963 papers

TitleStatusHype
Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments—0
Spectral complexity of deep neural networks—0
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
Wilsonian Renormalization of Neural Network Gaussian Processes—0
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data—0
Dynamic Online Ensembles of Basis ExpansionsCode0
Enhancing RSS-Based Visible Light Positioning by Optimal Calibrating the LED Tilt and Gain—0
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks—0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Markov Chain Monte Carlo with Gaussian Process Emulation for a 1D Hemodynamics Model of CTEPH—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations—0
A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures—0
Analytical results for uncertainty propagation through trained machine learning regression models—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
Spatio-Temporal Attention and Gaussian Processes for Personalized Video Gaze EstimationCode1
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales—0
Universal Functional Regression with Neural Operator FlowsCode1
Tensor Network-Constrained Kernel Machines as Gaussian ProcessesCode0
Learning Piecewise Residuals of Control Barrier Functions for Safety of Switching Systems using Multi-Output Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
Multi-Agent Clarity-Aware Dynamic Coverage with 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 tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Function-space Parameterization of Neural Networks for Sequential LearningCode0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
Learning High-Order Control Barrier Functions for Safety-Critical Control with Gaussian Processes—0
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
Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints—0
Chronos: Learning the Language of Time SeriesCode7
PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates—0
Explainable Learning with Gaussian ProcessesCode0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration—0
Robustness bounds on the successful adversarial examples in probabilistic models: Implications from Gaussian processes—0
Deep Horseshoe 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
Multi-Fidelity Residual Neural Processes for Scalable Surrogate ModelingCode1
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
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Benchmark Results

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