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 101–125 of 1963 papers

TitleStatusHype
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Causal Discovery via Bayesian OptimizationCode1
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging DataCode0
Diffusion-aware Censored Gaussian Processes for Demand ModellingCode0
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial dataCode0
Issues with Neural Tangent Kernel Approach to Neural NetworksCode0
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation—0
Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion—0
Multi-view Bayesian optimisation in reduced dimension for engineering design—0
Physics-informed Gaussian Processes for Safe Envelope ExpansionCode0
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes—0
Scalable Bayesian Optimization via Focalized Sparse Gaussian ProcessesCode0
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes—0
Bayesian Optimization of Bilevel Problems—0
Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational HypernetworksCode1
Fast Multi-Group Gaussian Process Factor Models—0
Comparing noisy neural population dynamics using optimal transport distances—0
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian OptimizationCode0
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural NetworksCode0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Bayesian Optimization via Continual Variational Last Layer Training—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
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Benchmark Results

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