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 251–275 of 1963 papers

TitleStatusHype
Automatic Construction and Natural-Language Description of Nonparametric Regression ModelsCode0
Accounting for Gaussian Process Imprecision in Bayesian OptimizationCode0
GPEX, A Framework For Interpreting Artificial Neural NetworksCode0
A Learnable Safety MeasureCode0
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process ModelsCode0
Autoencoder Attractors for Uncertainty EstimationCode0
Explainable Learning with Gaussian ProcessesCode0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Estimation of Dynamic Gaussian ProcessesCode0
Evolving-Graph Gaussian ProcessesCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation PropagationCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Entropic Trace Estimates for Log DeterminantsCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
The Debiased Spatial Whittle LikelihoodCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Show:102550
← PrevPage 11 of 79Next →

Benchmark Results

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