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 1851–1900 of 1963 papers

TitleStatusHype
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
When are Iterative Gaussian Processes Reliably Accurate?Code0
Deep Neural Architecture Search with Deep Graph Bayesian OptimizationCode0
Is Data All That Matters? The Role of Control Frequency for Learning-Based Sampled-Data Control of Uncertain SystemsCode0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process PerspectiveCode0
Issues with Neural Tangent Kernel Approach to Neural NetworksCode0
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion MethodCode0
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial DataCode0
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical ModelsCode0
Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian ProcessesCode0
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian ProcessesCode0
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural NetworksCode0
JUMBO: Scalable Multi-task Bayesian Optimization using Offline DataCode0
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process ApproachCode0
Deep Multi-fidelity Gaussian ProcessesCode0
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian ProcessesCode0
Active Learning for Deep Gaussian Process SurrogatesCode0
Additive Gaussian ProcessesCode0
Deep learning with differential Gaussian process flowsCode0
Deep Kernel Posterior Learning under Infinite Variance Prior WeightsCode0
Kernelized Capsule NetworksCode0
Kernel Learning for Explainable Climate ScienceCode0
Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and H_0 inferenceCode0
Deep Kernel Learning for Mortality Prediction in the Face of Temporal ShiftCode0
Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-ParametersCode0
Kernels for Vector-Valued Functions: a ReviewCode0
Stable behaviour of infinitely wide deep neural networksCode0
Knot Selection in Sparse Gaussian Processes with a Variational Objective FunctionCode0
Variational Fourier features for Gaussian processesCode0
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic FeaturesCode0
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural NetworksCode0
Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control TasksCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Deep Gaussian Processes with Importance-Weighted Variational InferenceCode0
Bayesian Modeling with Gaussian Processes using the GPstuff ToolboxCode0
Laplace Matching for fast Approximate Inference in Latent Gaussian ModelsCode0
Large Linear Multi-output Gaussian Process LearningCode0
Large-Scale Gaussian Processes via Alternating ProjectionCode0
Bayesian Meta-Learning Through Variational Gaussian ProcessesCode0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Last Layer Marginal Likelihood for Invariance LearningCode0
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical DataCode0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
Scalable Bayesian Optimization Using Deep Neural NetworksCode0
Latent variable model for high-dimensional point process with structured missingnessCode0
Latent Variable Multi-output Gaussian Processes for Hierarchical DatasetsCode0
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
Scalable Bayesian Optimization via Focalized Sparse Gaussian ProcessesCode0
Optimization as Estimation with Gaussian Processes in Bandit SettingsCode0
Show:102550
← PrevPage 38 of 40Next →

Benchmark Results

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