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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 101–150 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
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
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
Nonmyopic Global Optimisation via Approximate Dynamic ProgrammingCode0
Uncertainty Quantification for Transformer Models for Dark-Pattern Detection—0
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
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive controlCode2
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
MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference CalibrationCode1
Residual Deep Gaussian Processes on ManifoldsCode0
Robust Gaussian Processes via Relevance Pursuit—0
Inferring the Morphology of the Galactic Center Excess with Gaussian ProcessesCode0
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
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Benchmark Results

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