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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 1341–1350 of 1963 papers

TitleStatusHype
Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Deep Kernels with Probabilistic Embeddings for Small-Data LearningCode0
Nonstationary Multivariate Gaussian Processes for Electronic Health Records—0
Regularized Sparse Gaussian Processes—0
On the expected behaviour of noise regularised deep neural networks as Gaussian processes—0
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Deep Structured Mixtures of Gaussian ProcessesCode0
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Benchmark Results

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