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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 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
Bias-Free Scalable Gaussian Processes via Randomized TruncationsCode0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
Attentive Gaussian processes for probabilistic time-series generation—0
Latent Map Gaussian Processes for Mixed Variable Metamodeling—0
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty—0
Bandits for Learning to Explain from Explanations—0
Infinite-channel deep stable convolutional neural networks—0
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes—0
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals—0
Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach—0
Gaussian Experts Selection using Graphical Models—0
A probabilistic Taylor expansion with Gaussian processes—0
Gaussian Process Latent Class Choice Models—0
Faster Kernel Interpolation for Gaussian Processes—0
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application—0
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Model-based Policy Search for Partially Measurable Systems—0
Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control—0
A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes—0
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control—0
Improved active output selection strategy for noisy environments—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves—0
Gauss-Legendre Features for Gaussian Process Regression—0
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Benchmark Results

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