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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 151–175 of 1963 papers

TitleStatusHype
Active Bayesian Causal InferenceCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
Healing Products of Gaussian ProcessesCode1
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
Efficiently Sampling Functions from Gaussian Process PosteriorsCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
Implicit Gaussian process representation of vector fields over arbitrary latent manifoldsCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Building 3D Morphable Models from a Single ScanCode1
Causal Discovery via Bayesian OptimizationCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Learning safety in model-based Reinforcement Learning using MPC and Gaussian ProcessesCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
PriorVAE: Encoding spatial priors with VAEs for small-area estimationCode1
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Low-Precision Arithmetic for Fast Gaussian ProcessesCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for HealthcareCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Conditional Neural ProcessesCode1
Kernel Interpolation for Scalable Online Gaussian ProcessesCode1
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Benchmark Results

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