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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 701–725 of 1963 papers

TitleStatusHype
Extrinsic Bayesian Optimizations on Manifolds—0
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processesCode0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact SupervisionCode0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Information-Theoretic Safe Exploration with Gaussian ProcessesCode0
Deep Kernel Learning for Mortality Prediction in the Face of Temporal ShiftCode0
Gaussian Process Barrier States for Safe Trajectory Optimization and Control—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Frequency Domain Gaussian Process Models for H^ Uncertainties—0
Transductive Kernels for Gaussian Processes on Graphs—0
Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints—0
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking—0
Understanding Sparse Feature Updates in Deep Networks using Iterative Linearisation—0
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework—0
Counterfactual Learning with Multioutput Deep KernelsCode0
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models—0
Deep Gaussian Processes for Air Quality InferenceCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Challenges in Gaussian Processes for Non Intrusive Load MonitoringCode0
Introduction and Exemplars of Uncertainty Decomposition—0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification—0
Global Optimization with Parametric Function Approximation—0
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Benchmark Results

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