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 401–425 of 1963 papers

TitleStatusHype
Probabilistic Subgoal Representations for Hierarchical Reinforcement learningCode0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows—0
On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them—0
On the Consistency of Kernel Methods with Dependent Observations—0
Linearization Turns Neural Operators into Function-Valued Gaussian Processes—0
Approximation-Aware Bayesian Optimization—0
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes—0
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems—0
Demystifying Spectral Bias on Real-World Data—0
Stein Random Feature RegressionCode0
A Gaussian Process-based Streaming Algorithm for Prediction of Time Series With Regimes and OutliersCode0
Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning ApproachCode0
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian ProcessesCode0
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Gradients of Functions of Large MatricesCode0
Physically Consistent Modeling & Identification of Nonlinear Friction with Dissipative Gaussian Processes—0
Variance-Reducing Couplings for Random Features—0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization—0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial DataCode0
Regression Trees Know Calculus—0
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Benchmark Results

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