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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 526–550 of 1963 papers

TitleStatusHype
Neural-BO: A Black-box Optimization Algorithm using Deep Neural NetworksCode1
Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Random forests for binary geospatial data—0
Sharp Calibrated Gaussian Processes—0
Improved uncertainty quantification for neural networks with Bayesian last layerCode0
Gaussian processes at the Helm(holtz): A more fluid model for ocean currentsCode1
Guided Deep Kernel LearningCode1
Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator—0
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowCode2
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised LearningCode1
Gaussian Process-Gated Hierarchical Mixtures of ExpertsCode0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance LearningCode0
Towards Practical Preferential Bayesian Optimization with Skew Gaussian ProcessesCode1
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery—0
Learning Choice Functions with Gaussian ProcessesCode0
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes—0
Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions—0
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimizationCode0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spacesCode1
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Benchmark Results

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