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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 301–350 of 1963 papers

TitleStatusHype
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
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
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural NetworksCode0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Bayesian Optimization via Continual Variational Last Layer Training—0
Epidemiological Model Calibration via Graybox Bayesian Optimization—0
Uncertainty Quantification for Transformer Models for Dark-Pattern Detection—0
Nonmyopic Global Optimisation via Approximate Dynamic ProgrammingCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-ConceptCode0
Physics-informed Gaussian Processes as Linear Model Predictive Controller—0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Robust Bayesian Optimization via Localized Online Conformal PredictionCode0
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
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
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Benchmark Results

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