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

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Showing 501–550 of 1963 papers

TitleStatusHype
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEsCode1
Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average ConstraintsCode0
Actually Sparse Variational Gaussian ProcessesCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version—0
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
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
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
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
Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian ProcessesCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian ProcessesCode0
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