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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 901–925 of 1963 papers

TitleStatusHype
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning—0
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging—0
Bayesian Quantile and Expectile Optimisation—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Improving Random Forests by Smoothing—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes—0
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
Incremental Ensemble Gaussian Processes—0
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections—0
Incremental Structure Discovery of Classification via Sequential Monte Carlo—0
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Inducing Gaussian Process Networks—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Inference for Large Scale Regression Models with Dependent Errors—0
A Novel Gaussian Min-Max Theorem and its Applications—0
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Benchmark Results

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