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 201–225 of 1963 papers

TitleStatusHype
Analytical Results for the Error in Filtering of Gaussian Processes—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Analysis of Financial Credit Risk Using Machine Learning—0
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Analogical-based Bayesian Optimization—0
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes—0
A chain rule for the expected suprema of Gaussian processes—0
Adaptive Inducing Points Selection For Gaussian Processes—0
Bayesian Optimization via Continual Variational Last Layer Training—0
Bayesian Optimization with Informative Covariance—0
Amortized Variational Inference for Deep Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets—0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Adaptive finite element type decomposition of Gaussian processes—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Baryons from Mesons: A Machine Learning Perspective—0
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Benchmark Results

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