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 1351–1375 of 1963 papers

TitleStatusHype
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes—0
A Hybrid Approach for Trajectory Control Design—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
Algorithmic Linearly Constrained Gaussian Processes—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Aligned Multi-Task Gaussian Process—0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Amortized variance reduction for doubly stochastic objectives—0
Amortized Variational Inference for Deep Gaussian Processes—0
Analogical-based Bayesian Optimization—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Analysis of Financial Credit Risk Using Machine Learning—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Analytical Results for the Error in Filtering of Gaussian Processes—0
Analytical results for uncertainty propagation through trained machine learning regression models—0
An analytic comparison of regularization methods for Gaussian Processes—0
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Benchmark Results

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