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 651–675 of 1963 papers

TitleStatusHype
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
A probabilistic Taylor expansion with Gaussian processes—0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Fast emulation of density functional theory simulations using approximate Gaussian processes—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Faster Kernel Interpolation for Gaussian Processes—0
Gaussian Processes Over Graphs—0
Faster variational inducing input Gaussian process classification—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Fast Gaussian Process Regression for Big Data—0
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
Fast methods for training Gaussian processes on large data sets—0
Fast Multi-Group Gaussian Process Factor Models—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Combining Parametric Land Surface Models with Machine Learning—0
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture—0
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Benchmark Results

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