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 1651–1675 of 1963 papers

TitleStatusHype
Distribution Calibration for Regression—0
Double-descent curves in neural networks: a new perspective using Gaussian processes—0
Doubly infinite residual neural networks: a diffusion process approach—0
Doubly Sparse Variational Gaussian Processes—0
Dream to Explore: Adaptive Simulations for Autonomous Systems—0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Efficient Determination of Safety Requirements for Perception Systems—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Efficient Global Optimization using Deep Gaussian Processes—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks—0
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering—0
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
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Benchmark Results

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