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 651–675 of 1963 papers

TitleStatusHype
Dirichlet-based Gaussian Processes for Large-scale Calibrated ClassificationCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Active Learning for Deep Gaussian Process SurrogatesCode0
Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact SupervisionCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Direct loss minimization algorithms for sparse Gaussian processesCode0
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
Multi-resolution Multi-task Gaussian ProcessesCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Neural Non-Stationary Spectral KernelCode0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian ProcessesCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Federated Causal Inference from Observational DataCode0
Recovering BanditsCode0
Show:102550
← PrevPage 27 of 79Next →

Benchmark Results

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