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 401–450 of 1963 papers

TitleStatusHype
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical DataCode0
Latent variable model for high-dimensional point process with structured missingnessCode0
Data-driven Aerodynamic Analysis of Structures using Gaussian ProcessesCode0
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation PropagationCode0
Data-driven Approach for Interpolation of Sparse DataCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian ProcessesCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Learning Hyperparameters via a Data-Emphasized Variational ObjectiveCode0
Learning Nonparametric Volterra Kernels with Gaussian ProcessesCode0
Learning ODE Models with Qualitative Structure Using Gaussian ProcessesCode0
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian ProcessesCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Calibrated Computation-Aware Gaussian ProcessesCode0
Adversarial Robustness Guarantees for Random Deep Neural NetworksCode0
Autoencoder Attractors for Uncertainty EstimationCode0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive ControlCode0
A Learnable Safety MeasureCode0
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Automatic Construction and Natural-Language Description of Nonparametric Regression ModelsCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent SystemsCode0
Exact Gaussian Processes on a Million Data PointsCode0
Decomposing Gaussians with Unknown CovarianceCode0
Deconditional Downscaling with Gaussian ProcessesCode0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Evolving-Graph Gaussian ProcessesCode0
Explainable Learning with Gaussian ProcessesCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Manifold Gaussian Processes for RegressionCode0
Approximate Latent Force Model InferenceCode0
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite NetworksCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Deep convolutional Gaussian processesCode0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
Adaptive Basis Function Selection for Computationally Efficient PredictionsCode0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
All your loss are belong to BayesCode0
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Entropic Trace Estimates for Log DeterminantsCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Batch Bayesian Optimization via Local PenalizationCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
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Benchmark Results

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