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 1626–1650 of 1963 papers

TitleStatusHype
Differentially Private Gaussian Processes—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Differentiating the multipoint Expected Improvement for optimal batch design—0
Graph Based Gaussian Processes on Restricted Domains—0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Dimensionality Reduction as Probabilistic Inference—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Direct Integration of Recursive Gaussian Process Regression Into Extended Kalman Filters With Application to Vapor Compression Cycle Control—0
Dirichlet Logistic Gaussian Processes for Evaluation of Black-Box Stochastic Systems under Complex Requirements—0
Discovering and forecasting extreme events via active learning in neural operators—0
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks—0
Disentangling Trainability and Generalization in Deep Learning—0
Disentangling Trainability and Generalization in Deep Neural Networks—0
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks—0
Distributed Gaussian Processes—0
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes—0
Distributed non-parametric deep and wide networks—0
Distributional Gaussian Processes Layers for Out-of-Distribution Detection—0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces—0
Distributionally Robust Model Predictive Control with Mixture of Gaussian Processes—0
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Benchmark Results

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