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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1901–1925 of 1963 papers

TitleStatusHype
Scalable Bayesian Preference Learning for CrowdsCode0
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act ClassificationCode0
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal dataCode0
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision MakingCode0
TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity AnalysisCode0
Orthogonally Decoupled Variational Gaussian ProcessesCode0
Learning Choice Functions with Gaussian ProcessesCode0
Output-Weighted Sampling for Multi-Armed Bandits with Extreme PayoffsCode0
Learning Constrained Dynamics with Gauss Principle adhering Gaussian ProcessesCode0
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian ProcessesCode0
Turbocharging Gaussian Process Inference with Approximate Sketch-and-ProjectCode0
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product NetworksCode0
Visual Pursuit Control based on Gaussian Processes with Switched Motion TrajectoriesCode0
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian ProcessesCode0
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train DecompositionCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial dataCode0
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization BoundsCode0
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix ApproximationsCode0
Learning Hyperparameters via a Data-Emphasized Variational ObjectiveCode0
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian ProcessesCode0
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Benchmark Results

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