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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 351–400 of 1963 papers

TitleStatusHype
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation—0
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty—0
GP+: A Python Library for Kernel-based learning via Gaussian ProcessesCode1
Sparse Variational Student-t Processes—0
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes—0
Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Scalable Meta-Learning with Gaussian Processes—0
Estimation of Dynamic Gaussian ProcessesCode0
Gaussian Processes for Monitoring Air-Quality in KampalaCode0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization—0
Variational Elliptical Processes—0
BOIS: Bayesian Optimization of Interconnected Systems—0
Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)—0
Spatial Bayesian Neural NetworksCode0
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options—0
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftCode2
Sound field reconstruction using neural processes with dynamic kernelsCode1
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Solving High Frequency and Multi-Scale PDEs with Gaussian ProcessesCode1
Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and H_0 inferenceCode0
Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics—0
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes—0
Gaussian Processes on Cellular Complexes—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Variational Gaussian Processes For Linear Inverse Problems—0
Robust and Conjugate Gaussian Process RegressionCode0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Stochastic Gradient Descent for Gaussian Processes Done RightCode1
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Hodge-Compositional Edge Gaussian ProcessesCode0
Deep Transformed Gaussian Processes—0
Large-Scale Gaussian Processes via Alternating ProjectionCode0
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regressionCode0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Thin and Deep Gaussian ProcessesCode1
Gaussian processes based data augmentation and expected signature for time series classification—0
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models—0
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies—0
Infinite Width Graph Neural Networks for Node Regression/ ClassificationCode0
Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes—0
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
Stationarity without mean reversion in improper Gaussian processes—0
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints—0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
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Benchmark Results

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