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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

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Showing 1176–1200 of 1963 papers

TitleStatusHype
Gaussian Process Molecule Property Prediction with FlowMO—0
Predicting the Outputs of Finite Networks Trained with Noisy Gradients—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Stein Variational Gaussian ProcessesCode0
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers—0
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels—0
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children—0
Contraction L_1-Adaptive Control using Gaussian Processes—0
Information Theoretic Meta Learning with Gaussian Processes—0
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion MethodCode0
Modulating Scalable Gaussian Processes for Expressive Statistical LearningCode0
Locally induced Gaussian processes for large-scale simulation experiments—0
Fast Approximate Multi-output Gaussian ProcessesCode0
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization—0
Preferential Bayesian optimisation with Skew Gaussian Processes—0
Continuous Optimization Benchmarks by SimulationCode0
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling—0
Multi-Agent Safe Planning with Gaussian Processes—0
Deterministic error bounds for kernel-based learning techniques under bounded noiseCode0
Do ideas have shape? Idea registration as the continuous limit of artificial neural networksCode0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes—0
Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression—0
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes—0
Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard AssessmentCode0
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Benchmark Results

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