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 1451–1500 of 1963 papers

TitleStatusHype
Mixtures of Gaussian Process Experts with SMC^2—0
Physics-Based Learning for Robotic Environmental Sensing—0
Model-based Policy Search for Partially Measurable Systems—0
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application—0
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow—0
Modeling and interpolation of the ambient magnetic field by Gaussian processes—0
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version—0
Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing—0
Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes—0
Modeling human function learning with Gaussian processes—0
Modeling Severe Traffic Accidents With Spatial And Temporal Features—0
Modeling the evolution of temporal knowledge graphs with uncertainty—0
Modelling Annotator Bias with Multi-task Gaussian Processes: An Application to Machine Translation Quality Estimation—0
Modelling Human Active Search in Optimizing Black-box Functions—0
Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes—0
Modelling Representation Noise in Emotion Analysis using Gaussian Processes—0
Modelling spatio-temporal trends of air pollution in Africa—0
Modelling Uncertainty in Collaborative Document Quality Assessment—0
Modelling variability in vibration-based PBSHM via a generalised population form—0
Model of rough surfaces with Gaussian processes—0
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles—0
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
Modulating Surrogates for Bayesian Optimization—0
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes—0
Monte Carlo inference for semiparametric Bayesian regression—0
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models—0
Monte Carlo Tree Descent for Black-Box Optimization—0
Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments—0
Motor cortex mapping using active gaussian processes—0
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints—0
Multi-Agent Clarity-Aware Dynamic Coverage with Gaussian Processes—0
Multi-Agent Safe Planning with Gaussian Processes—0
Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection—0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Leveraging Probabilistic Circuits for Nonparametric Multi-Output RegressionCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy SearchCode0
Likelihood-Free Inference with Deep Gaussian ProcessesCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Estimation of Dynamic Gaussian ProcessesCode0
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm DesignCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervalsCode0
The Shape of Learning Curves: a ReviewCode0
Physics-informed Gaussian Processes for Safe Envelope ExpansionCode0
Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal dataCode0
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Benchmark Results

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