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 126–150 of 1963 papers

TitleStatusHype
Active Testing: Sample-Efficient Model EvaluationCode1
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive ControlCode1
ILoSA: Interactive Learning of Stiffness and AttractorsCode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Kernel Interpolation for Scalable Online Gaussian ProcessesCode1
On Feature Collapse and Deep Kernel Learning for Single Forward Pass UncertaintyCode1
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental LearningCode1
High-Dimensional Gaussian Process Inference with DerivativesCode1
Healing Products of Gaussian ProcessesCode1
Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 DataCode1
Convolutional conditional neural processes for local climate downscalingCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
Task-Agnostic Amortized Inference of Gaussian Process HyperparametersCode1
Stochastic Deep Gaussian Processes over GraphsCode1
Exploration in Online Advertising Systems with Deep Uncertainty-Aware LearningCode1
Building 3D Morphable Models from a Single ScanCode1
Pathwise Conditioning of Gaussian ProcessesCode1
Transforming Gaussian Processes With Normalizing FlowsCode1
High-Dimensional Bayesian Optimization via Nested Riemannian ManifoldsCode1
Probabilistic Numeric Convolutional Neural NetworksCode1
Probabilistic selection of inducing points in sparse Gaussian processesCode1
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspacesCode1
Recyclable Gaussian ProcessesCode1
Multi-task Causal Learning with Gaussian ProcessesCode1
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Benchmark Results

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