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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1901–1950 of 1963 papers

TitleStatusHype
Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial—0
Input Warping for Bayesian Optimization of Non-stationary Functions—0
Accelerating ABC methods using Gaussian processes—0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
Associative embeddings for large-scale knowledge transfer with self-assessment—0
Multi-Task Bayesian Optimization—0
Bayesian optimization explains human active search—0
Gaussian Process Optimization with Mutual Information—0
Nonparametric Bayes dynamic modeling of relational data—0
Active Learning of Linear Embeddings for Gaussian Processes—0
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes—0
Pseudo-Marginal Bayesian Inference for Gaussian Processes—0
A temporal model of text periodicities using Gaussian Processes—0
Reasoning about Probabilities in Dynamic Systems using Goal Regression—0
Modelling Annotator Bias with Multi-task Gaussian Processes: An Application to Machine Translation Quality Estimation—0
Infinite Mixtures of Multivariate Gaussian Processes—0
Bayesian Structured Prediction Using Gaussian ProcessesCode0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix ApproximationsCode0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes—0
Gaussian Processes for Nonlinear Signal Processing—0
Gaussian Process Kernels for Pattern Discovery and ExtrapolationCode0
Multiresolution Gaussian Processes—0
Bayesian Warped Gaussian Processes—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Collaborative Gaussian Processes for Preference Learning—0
Random walk kernels and learning curves for Gaussian process regression on random graphs—0
Deep Gaussian Processes—0
Locally adaptive factor processes for multivariate time series—0
Information fusion in multi-task Gaussian processes—0
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Bayesian Modeling with Gaussian Processes using the GPstuff ToolboxCode0
Robust Filtering and Smoothing with Gaussian Processes—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Additive Gaussian ProcessesCode0
Analytical Results for the Error in Filtering of Gaussian Processes—0
Universal low-rank matrix recovery from Pauli measurements—0
Active learning of neural response functions with Gaussian processes—0
Nonlinear Inverse Reinforcement Learning with Gaussian ProcessesCode0
Optimal Reinforcement Learning for Gaussian Systems—0
Gaussian Process Regression NetworksCode0
Overlapping Mixtures of Gaussian Processes for the Data Association Problem—0
Linear Latent Force Models using Gaussian Processes—0
Kernels for Vector-Valued Functions: a ReviewCode0
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors—0
Heavy-Tailed Process Priors for Selective Shrinkage—0
Evaluation of Rarity of Fingerprints in Forensics—0
Approximate inference in continuous time Gaussian-Jump processes—0
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Benchmark Results

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