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 76–100 of 1963 papers

TitleStatusHype
Causal Discovery via Bayesian OptimizationCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Deep Kernel LearningCode1
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial PredictionCode1
Deep Pipeline Embeddings for AutoMLCode1
Dense Gaussian Processes for Few-Shot SegmentationCode1
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational AutoencodersCode1
Supervising the Multi-Fidelity Race of Hyperparameter ConfigurationsCode1
Efficiently Sampling Functions from Gaussian Process PosteriorsCode1
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Gaussian Processes for Missing Value ImputationCode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processesCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
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Benchmark Results

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