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 326–350 of 1963 papers

TitleStatusHype
Privacy Preserving Federated Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation DataCode0
Gaussian Process Priors for Boundary Value Problems of Linear Partial Differential EquationsCode0
Sparsifying Suprema of Gaussian Processes—0
Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems—0
Constructing Gaussian Processes via Samplets—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
A spectral mixture representation of isotropic kernels to generalize random Fourier features—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Robust Gaussian Processes via Relevance Pursuit—0
Residual Deep Gaussian Processes on ManifoldsCode0
Inferring the Morphology of the Galactic Center Excess with Gaussian ProcessesCode0
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian ProcessesCode0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
Nonlinear bayesian tomography of ion temperature and velocity for Doppler coherence imaging spectroscopy in RT-1—0
Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervalsCode0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
Data-Driven Approaches for Modelling Target Behaviour—0
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure—0
Calibrated Computation-Aware Gaussian ProcessesCode0
Online scalable Gaussian processes with conformal prediction for guaranteed coverage—0
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
Show:102550
← PrevPage 14 of 79Next →

Benchmark Results

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