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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 651–700 of 1963 papers

TitleStatusHype
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version—0
Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training—0
Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation—0
Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian ProcessesCode0
Neural signature kernels as infinite-width-depth-limits of controlled ResNetsCode0
Sparse Gaussian Processes with Spherical Harmonic Features Revisited—0
GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian ManifoldsCode0
Stochastic Model Predictive Control Utilizing Bayesian Neural Networks—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality—0
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary SystemsCode0
Gaussian Process on the Product of Directional Manifolds—0
Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning—0
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics—0
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles—0
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearityCode0
Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes—0
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian ProcessesCode0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Random forests for binary geospatial data—0
Sharp Calibrated Gaussian Processes—0
Improved uncertainty quantification for neural networks with Bayesian last layerCode0
Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
Gaussian Process-Gated Hierarchical Mixtures of ExpertsCode0
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance LearningCode0
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes—0
Learning Choice Functions with Gaussian ProcessesCode0
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery—0
Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimizationCode0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Sequential Estimation of Gaussian Process-based Deep State-Space Models—0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and PredictionCode0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
On the role of Model Uncertainties in Bayesian Optimization—0
Modeling the evolution of temporal knowledge graphs with uncertainty—0
Machine learning methods for prediction of breakthrough curves in reactive porous media—0
Robust Bayesian Target Value Optimization—0
Application of machine learning to gas flaring—0
Quantile Autoregression-based Non-causality Testing—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
Robust and Scalable Gaussian Process Regression and Its ApplicationsCode0
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential EquationsCode0
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Benchmark Results

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