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 976–1000 of 1963 papers

TitleStatusHype
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning—0
Random forests for binary geospatial data—0
Random Neural Networks in the Infinite Width Limit as Gaussian Processes—0
Random ReLU Neural Networks as Non-Gaussian Processes—0
Random walk kernels and learning curves for Gaussian process regression on random graphs—0
Rate of Convergence of Polynomial Networks to Gaussian Processes—0
Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape—0
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models—0
Real-Time Regression with Dividing Local Gaussian Processes—0
Reasoning about Probabilities in Dynamic Systems using Goal Regression—0
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes—0
Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning—0
The Schrödinger Bridge between Gaussian Measures has a Closed Form—0
Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints—0
Recurrent Memory for Online Interdomain Gaussian Processes—0
Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes—0
Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach—0
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization—0
Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference—0
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities—0
Regression Trees Know Calculus—0
Regression with Linear Factored Functions—0
Regret Bound for Safe Gaussian Process Bandit Optimization—0
Regret Bounds for Safe Gaussian Process Bandit Optimization—0
Regularized Sparse Gaussian Processes—0
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Benchmark Results

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