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 651–700 of 1963 papers

TitleStatusHype
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
A probabilistic Taylor expansion with Gaussian processes—0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Fast emulation of density functional theory simulations using approximate Gaussian processes—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Faster Kernel Interpolation for Gaussian Processes—0
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
Faster variational inducing input Gaussian process classification—0
Bayesian Warped Gaussian Processes—0
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Fast Gaussian Process Regression for Big Data—0
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
Fast methods for training Gaussian processes on large data sets—0
Fast Multi-Group Gaussian Process Factor Models—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Combining Parametric Land Surface Models with Machine Learning—0
Few-shot Learning for Spatial Regression—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes—0
Finite size corrections for neural network Gaussian processes—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Comparing noisy neural population dynamics using optimal transport distances—0
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems—0
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting—0
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood—0
Forecasting of commercial sales with large scale Gaussian Processes—0
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes—0
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging—0
Frequency Domain Gaussian Process Models for H^ Uncertainties—0
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
Compositionally-Warped Gaussian Processes—0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
A Perspective on Gaussian Processes for Earth Observation—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification—0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Bayesian Variational Optimization for Combinatorial Spaces—0
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Benchmark Results

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