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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 801–850 of 1963 papers

TitleStatusHype
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Linear-time inference for Gaussian Processes on one dimension—0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Gene Regulatory Network Inference with Latent Force Models—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Geometry-Aware Hierarchical Bayesian Learning on Manifolds—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction—0
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
Global Optimization with Parametric Function Approximation—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Bayesian Warped Gaussian Processes—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
A Perspective on Gaussian Processes for Earth Observation—0
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes—0
Bayesian Variational Optimization for Combinatorial Spaces—0
A computationally lightweight safe learning algorithm—0
GP Kernels for Cross-Spectrum Analysis—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Efficient Global Optimization using Deep Gaussian Processes—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Gradient-enhanced deep Gaussian processes for multifidelity modelling—0
Data Efficient Prediction of excited-state properties using Quantum Neural Networks—0
Granger Causality from Quantized Measurements—0
Graph Classification Gaussian Processes via Spectral Features—0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Graph Convolutional Gaussian Processes For Link Prediction—0
Genus expansion for non-linear random matrix ensembles with applications to neural networks—0
Graphical LASSO Based Model Selection for Time Series—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
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Benchmark Results

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