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 1051–1075 of 1963 papers

TitleStatusHype
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes—0
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization—0
Gaussian Process Convolutional Dictionary Learning—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Raven's Progressive Matrices Completion with Latent Gaussian Process PriorsCode0
Data-driven Aerodynamic Analysis of Structures using Gaussian ProcessesCode0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
The Shape of Learning Curves: a ReviewCode0
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey—0
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning—0
On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach—0
Small Sample Spaces for Gaussian Processes—0
Fast Adaptation with Linearized Neural Networks—0
Hierarchical Inducing Point Gaussian Process for Inter-domain ObservationsCode0
Similarity measure for sparse time course data based on Gaussian processesCode0
The Promises and Pitfalls of Deep Kernel Learning—0
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference—0
Large-width functional asymptotics for deep Gaussian neural networks—0
Output-Weighted Sampling for Multi-Armed Bandits with Extreme PayoffsCode0
Non-asymptotic approximations of neural networks by Gaussian processes—0
Using Distance Correlation for Efficient Bayesian Optimization—0
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients—0
Double-descent curves in neural networks: a new perspective using Gaussian processes—0
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Benchmark Results

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