Machine learning speeds up GPR simulations.
problem Computational demands of simulating practical GPR problems.
method Automatic ML-based forward solver framework using gprMax.
result Near-real-time GPR simulations achieved.
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48 results for “GPR”
Accelerates GPR with localized kernels for faster performance.
problem Speeding up Gaussian process regression.
method Localization kernels applied at each data point to down-weight distant points, leading to a sparsified Gram matrix.
result Significant speedups with competitive performance compared to other methods.
NN-GPR improves climate model predictions by preserving fine-scale spatial information.
problem Dilution of fine-scale spatial information and bias in model averaging.
method Gaussian process regression with an infinitely wide deep neural network.
result NN-GPR produces more accurate and detailed climate projections.
This tutorial simplifies Gaussian process regression for beginners.
problem Understanding Gaussian process regression for machine learning.
method Explains basic concepts, provides a concise GPR description, and reviews implementation packages.
result Clear understanding of Gaussian process regression fundamentals.
Proposes GPR with local explanation for feature interpretation.
problem Lack of feature interpretation in Gaussian process regression.
method Introduces a locally linear model to explain feature contributions in GPR predictions.
result Achieves comparable predictive performance to GPR and superior interpretability.
PhI-GPR improves power grid state estimation and forecasting.
problem Accurate state estimation and forecasting in power grids with sparse measurements.
method Physics-informed Gaussian process regression (PhI-GPR) for stochastic differential equations.
result PhI-GPR provides more accurate forecasts and estimates of power grid states compared to ARIMA.
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Datacs.LG
EVARS-GPR refines Gaussian Process Regression for seasonal data with sudden scale changes.
problem Challenges in forecasting with changing system behavior over time.
method Combines online change point detection with data augmentation for refitting.
result 20.8% lower RMSE on real-world datasets compared to similar methods.
We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent. We develop gradient-based inference methods to learn the unknown function and the non-stationary model param…
Adaptive GPR-GNN optimizes node feature and topology learning.
problem Optimizing GNNs for both node features and graph topology, regardless of homophily or heterophily.
method Adaptive Universal Generalized PageRank (GPR) Graph Neural Network (GPR-GNN) that learns optimal GPR weights.
result Significant performance improvement on node classification tasks compared to state-of-the-art GNNs.
Paper speeds up visualization of uncertain data.
problem High computational cost in reconstructing data uncertainties.
method Subdivide data spatially, adaptively reconstructing only necessary values, using GPR kernel and saved data observations to estimate upper bounds for level-crossing probabilities.
result Accurate estimation of value occurrence probabilities with low computation cost.
GPR ensemble method predicts stock returns efficiently.
problem Predicting stock returns using machine learning.
method Ensemble Gaussian Process Regression (GPR) for online learning.
result Method outperforms existing models in -squared and Sharpe ratio.
The paper provides tighter error bounds for GPR under bounded support noise.
problem Rigorous error quantification for safety-critical applications with bounded noise.
method Using concentration inequalities and low complexity assumptions in RKHS, the paper derives probabilistic and deterministic error bounds for GPR.
result The derived error bounds are substantially tighter than existing state-of-the-art bounds and are particularly well-suited for GPR with neural network kernels.
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generat…
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner. Here we present a novel method for ambulance demand prediction using Gaussian process regression (GPR) in time and geographic space. The method exhibits superior accuracy to MEDIC…
Paper improves SVaR estimation for stress testing under macro scenarios using a hybrid GPR-HS framework.
problem Numerical instability in traditional SVaR estimation under extreme shocks.
method Extends GPR-HS framework to forward-looking stress scenarios with SACS for stable covariance.
result Stable SVaR ranges from -2.1020% to -2.2231%, preserving coherence property.
GPR models epidemic spread on logarithmic scale.
problem Modeling and predicting epidemic spread for policy decisions.
method Gaussian process regression (GPR) on logarithmic scale of infected cases.
result GPR predictions have high probability of being within 95% confidence interval for 94.29% of data.
Machine Learning for Pricing American Options in High-Dimensional Markovian and non-Markovian modelsq-fin.CP
In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black-Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based …
Paper introduces a hybrid GPR model for more interpretable RUL prediction in aeroengine.
problem Challenges in interpreting and modeling uncertainty in RUL prediction models.
method Modified Gaussian Process Regression (GPR) with temporal feature extraction.
result Effective prediction of RUL intervals with transparent feature significance.
Two approaches extend knowledge distillation to Gaussian Processes, showing relationships to existing methods.
problem Applying knowledge distillation to Gaussian Processes for regression and classification.
method Data-centric and distribution-centric approaches to extend distillation to GPR and GPC.
result Distribution-centric approach for GPC approximately corresponds to data duplication and scaling.
The hyperparameters in Gaussian process regression (GPR) model with a specified kernel are often estimated from the data via the maximum marginal likelihood. Due to the non-convexity of marginal likelihood with respect to the hyperparameters, the optimization may not converge to the global maxima. A common approach to …
Traffic flow forecasting, especially the short-term case, is an important topic in intelligent transportation systems (ITS). This paper does a lot of research on network-scale modeling and forecasting of short-term traffic flows. Firstly, we propose the concepts of single-link and multi-link models of traffic flow fore…
Efficient method for pricing Bermudan moving average options using GPR-GHQ.
problem High-dimensional pricing of Bermudan moving average options in energy markets.
method Gaussian Process Regression and Gauss-Hermite quadrature.
result GPR-GHQ method efficiently handles long windows and high dimensionality.
SVM methods improve tack coat classification in French pavements.
problem Classifying thin tack coat layers in GPR data.
method Inverse Machine Learning approach applied to SVM/SVR methods.
result SVM/SVR methods accurately classify tack coat emulsion proportions.
The paper introduces a knowledge score for GPR predictions to assess their reliability.
problem Uncertainty in probabilistic predictions from Gaussian process regression models.
method A knowledge score quantifying the reduction of uncertainty in GPR predictions.
result The knowledge score improves prediction accuracy in various tasks.
A hybrid physics-ML model predicts FO water flux with high accuracy and uncertainty quantification.
problem Challenges in accurately modeling Forward Osmosis water flux due to complex internal mass transfer phenomena.
method Robust Hybrid Physics-ML framework using Gaussian Process Regression (GPR) for uncertainty-aware Jw prediction.
result Achieved a state-of-the-art MAPE of 0.26% and R2 of 0.999 on independent test data.
Study finds multifractal cross-correlations between agricultural markets and external uncertainties.
problem Investigating relationships between agricultural spot markets and external uncertainties.
method Multifractal detrending moving-average cross-correlation analysis (MF-X-DMA).
result Maize exhibits intrinsic joint multifractality with all uncertainty proxies.
A two-stage GPR framework with automatic kernel search and subsampling improves prediction accuracy and efficiency.
problem Inaccurate predictions due to misspecified mean and kernel functions in Gaussian Process Regression.
method Two-stage GPR, automatic kernel search, subsampling for hyperparameter initialization.
result Competitive or better performance compared to full dataset training, robust on real-world datasets.
This paper improves active learning for Gaussian process regression to handle distributional uncertainty.
problem Active learning for Gaussian process regression does not guarantee accurate predictions for target distributions.
method Proposes two methods to reduce worst-case expected error for Gaussian process regression.
result Shows an upper bound of the worst-case expected squared error, suggesting finite data labels can achieve arbitrarily small error.
Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing with large data sets. Here, we derive some simple results which we h…
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernelsphysics.comp-ph
GPR enhances materials discovery by automating parameter space exploration.
problem Automating exploration of large, high-dimensional parameter spaces in materials science.
method Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels.
result Importance and benefits of tuning GPR for materials science experiments.
Paper proposes a new GPR-HS framework for accurate VCV estimation in global equity indices.
problem Accurate forecasting of Volatility-Covariance Matrix (VCV) for regulatory processes.
method Hybrid Gaussian Process Regression-Historical Simulation (GPR-HS) framework.
result GPR-HS framework achieves regulatory compliance and outperforms static VaR benchmarks.
Bayesian GPR model predicts extreme stock market losses.
problem Forecasting rare but impactful extreme negative returns in equity markets.
method Developed a Bayesian Generalised Pareto Regression model linking scale parameter to market volatility.
result The Cauchy prior provides the best balance between predictive accuracy and model simplicity.
RS-HDMR-GPR simplifies complex functions with machine-learned lower-dimensional terms.
problem Representing and understanding complex multidimensional functions with sparse data.
method Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR).
result Facilitates recovery of functional dependence and adds insight into input variable importance.
We first introduce a novel profile-based alignment algorithm, the multiple continuous Signal Alignment algorithm with Gaussian Process Regression profiles (SA-GPR). SA-GPR addresses the limitations of currently available signal alignment methods by adopting a hybrid of the particle smoothing and Markov-chain Monte Carl…
Active learning reduces SP calculations by 90%.
problem Efficiently calculating saddle points in energy functions.
method Active learning framework with GPR and GAD.
result Significant reduction in the number of expensive evaluations.
Can we recover a complex signal from its Fourier magnitudes? More generally, given a set of measurements, for , is it possible to recover (i.e., length- complex vector)? This **generalized phase retrieval** (GPR) problem is a fundame…
Study forecasts Turkish residential NGD using JITL-GPR, reducing errors.
problem Accurately predict future monthly NGD for Turkey's import contracts.
method Used historical NG consumption data, applied various time series models, and introduced JITL-GPR.
result JITL-GPR reduces forecast errors compared to traditional methods.
KPCA-BO improves BO for high-dimensional optimization problems by learning a non-linear sub-manifold.
problem High-dimensional optimization problems where Gaussian Process regression requires too much data and computation.
method KPCA-BO embeds a non-linear sub-manifold in the search space, learning a GPR model on this sub-manifold.
result KPCA-BO outperforms vanilla BO in convergence speed, especially as dimensionality increases.
Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to re-formulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, re-formulation is not always workable a…
Study uses AI and ML to predict and optimize corrosion resistance of aluminum alloys.
problem Corrosion resistance of aluminum alloys in marine environments.
method Investigated two ML approaches: direct and inverse, using Random Forest, neural network, and Gaussian Process Regression.
result Gaussian Process Regression with hybrid kernel functions provided superior predictive performance.
Kernel Multigrid accelerates Back-fitting for additive Gaussian Processes.
problem Slow convergence of Back-fitting in training additive Gaussian Processes.
method Kernel Packets (KP) and Sparse Gaussian Process Regression (GPR) to enhance Back-fitting.
result Kernel Multigrid reduces the required iterations to .
Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of human lives lost due to this phenomenon has been a strong motivation for the research community toward the development of safe and robust techniques…
Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called \textit{Type II maximum likelihood} or ML-II). An alternative learning proced…
Tensor network surrogate for efficient option pricing in large portfolios.
problem Large-scale portfolio revaluation problems in market risk management.
method Tensor-train (TT) approximation for high-dimensional price surfaces, direct inference using Laplacian kernel and TT representations.
result Tensor surrogate achieves lower test error and faster evaluation times compared to standard GPR.
Study compares deterministic and probabilistic ML for precise AM component dimensions.
problem Accurately estimate dimensions of additively manufactured parts with variability.
method Employed models integrating continuous and categorical factors, tested deterministic and probabilistic ML methods.
result Gaussian Process Regression and Bayesian Neural Networks provide strong predictive performance and uncertainty quantification.
SHARC explains machine learning risk models for regulatory capital, linking outputs to scenarios.
problem Inability to explain machine learning model outputs to regulatory bodies.
method SHAP-based explainability framework for Hybrid GPR-HS architecture and SVaR stress-testing.
result SHARC links SVaR outputs to scenario inputs, providing auditable traceability.
This paper offers a financial economic perspective on the optimal time (and age) at which the owner of a Variable Annuity (VA) policy with a Guaranteed Living Withdrawal Benefit (GLWB) rider should initiate guaranteed lifetime income payments. We abstract from utility, bequest and consumption preference issues by treat…
In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this a…