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 151–200 of 1963 papers

TitleStatusHype
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
ILoSA: Interactive Learning of Stiffness and AttractorsCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational HypernetworksCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Exploration in Online Advertising Systems with Deep Uncertainty-Aware LearningCode1
Kernel Interpolation for Scalable Online Gaussian ProcessesCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Deep Kernel LearningCode1
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)Code1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Matérn Gaussian processes on Riemannian manifoldsCode1
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 DataCode1
Conditional Neural ProcessesCode1
Model-Based Transfer Learning for Contextual Reinforcement LearningCode1
Modular Gaussian Processes for Transfer LearningCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspacesCode1
Multi-Fidelity Residual Neural Processes for Scalable Surrogate ModelingCode1
Convolutional conditional neural processes for local climate downscalingCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Gaussian processes at the Helm(holtz): A more fluid model for ocean currentsCode1
Neural Networks and Quantum Field TheoryCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Nonnegative spatial factorizationCode1
Operator Learning with Gaussian ProcessesCode1
Optimizing Hyperparameters with Conformal Quantile RegressionCode1
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEsCode1
Physics Inspired Approaches To Understanding Gaussian ProcessesCode1
Posterior and Computational Uncertainty in Gaussian ProcessesCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive ControlCode1
Probabilistic Recurrent State-Space ModelsCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Quadruply Stochastic Gaussian ProcessesCode1
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional StructureCode1
Scalable Exact Inference in Multi-Output Gaussian ProcessesCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Deep Pipeline Embeddings for AutoMLCode1
SEAL: Simultaneous Exploration and Localization in Multi-Robot SystemsCode1
Semi-Supervised Image Deraining using Gaussian ProcessesCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
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Benchmark Results

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