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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 226–250 of 1963 papers

TitleStatusHype
Fast Approximate Multi-output Gaussian ProcessesCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Explainable Learning with Gaussian ProcessesCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
All your loss are belong to BayesCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Entropic Trace Estimates for Log DeterminantsCode0
Federated Causal Inference from Observational DataCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Adaptive Basis Function Selection for Computationally Efficient PredictionsCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Estimation of Dynamic Gaussian ProcessesCode0
Bayesian Deep Learning on a Quantum ComputerCode0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Avoiding pathologies in very deep networksCode0
Functional Variational Bayesian Neural NetworksCode0
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite NetworksCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
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Benchmark Results

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