arXiv research

Study estimates personalized effects of maternal PM2.5 exposure on birth weight.

Measurement error in observational datasets can lead to systematic bias in inferences based on these datasets. As studies based on observational data are increasingly used to inform decisions with real-world impact, it is critical that we develop a robust set of techniques for analyzing and adjusting for these biases. …

There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal …

The NNGP kernel's predictions closely match those of the Matern kernel under certain conditions.

New algorithm optimizes functions in Matérn kernel RKHS with noisy feedback.

The Matérn covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the Matérn class is that it is possible to get precise control over the degree of mean-square differentiability of the random process. However, the Matérn class possesses e…

Graph Gaussian processes use Matérn models for better function learning.

Proposes a new model for traffic flow on directed graphs.

Exact and scalable algorithm for Gaussian process regression with Matérn correlations.

Approximating Gaussian Whittle-Matern Fields over Well-Centered Triangulations of Riemannian Manifolds

The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we face both challe…

Optimizes Gaussian process hyperparameters using Bayesian autoregression.

New Hida-Matérn kernels enable flexible process priors and efficient GP inference.

Paper speeds up Gaussian process inference using Matérn kernels.

The paper explores the identifiability and interpretability of Gaussian process models using different kernel structures.

New Gaussian processes for Riemannian manifolds enable uncertainty quantification.

Linear cost method approximates Gaussian Matérn processes with exponentially convergent accuracy.

The paper improves error bounds for Bayesian quadrature in noisy settings.

Study finds polynomial convergence rate for Farey sequences linked to Riemann hypothesis.

New approach tackles non-Markovian behavior in maternal health programs.

Green stocks show less factor exposure heterogeneity compared to brown stocks.

cvHM framework speeds up GP inference for neural spike train analysis.

App enhances midwives' skills in low-income countries.

We conduct a study of the aliased spectral densities of Matérn covariance functions on a regular grid of points, providing clarity on the properties of a popular approximation based on stochastic partial differential equations; while others have shown that it can approximate the covariance function well, we find that i…

The paper establishes bounds on the smoothness parameter in Gaussian process interpolation.

For the challenging task of modeling multivariate time series, we propose a new class of models that use dependent Matérn processes to capture the underlying structure of data, explain their interdependencies, and predict their unknown values. Although similar models have been proposed in the econometric, statistics, a…

This study examines Gaussian processes on Riemannian manifolds and proves contraction rates.

Data science predicts user interest for midwifery content.

We consider the problem of Bayesian optimization (BO) in one dimension, under a Gaussian process prior and Gaussian sampling noise. We provide a theoretical analysis showing that, under fairly mild technical assumptions on the kernel, the best possible cumulative regret up to time $T$ behaves as $Ω(\sqrt{T})$ and $O(\s…

ConvNets improve nonstationary covariance estimation for large-scale spatial data.

Deep learning approximates Bermudan option exposures and future values.

Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.

Randomized neural networks improve exposure and CVA estimation for American options.

Standard Gaussian Process outperforms in high-dimensional Bayesian Optimization.

Valuation of Credit Valuation Adjustment (CVA) has become an important field as its calculation is required in Basel III, issued in 2010, in the wake of the credit crisis. Exposure, which is defined as the potential future loss of a default event without any recovery, is one of the key elementsfor pricing CVA. This pap…

Derives a Matern Gaussian process on hypergraphs for regression and embedding.

Paper analyzes consistency of Bayesian and machine learning methods for hierarchical parameter estimation.

Paper optimizes neural networks for Bermudan option pricing with faster convergence and risk management tools.

Exposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy. Although a lot of algorithms have been proposed to avoid teacher forcing and theref…

Bayesian framework for sphere regression using Gaussian fields.

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situati…

GP-UCB resolves sublinear regret for kernelized bandits.

We study the impact of central clearing of over-the-counter (OTC) transactions on counterparty exposures in a market with OTC transactions across several asset classes with heterogeneous characteristics. The impact of introducing a central counterparty (CCP) on expected interdealer exposure is determined by the tradeof…

The paper proposes using function approximations to reduce the computational burden in measuring counterparty credit exposure.

Study prenatal PM2.5 exposure and 4th grade reading scores, identifying critical windows of susceptibility.

Mack's estimator improves chain ladder prediction for large exposure insurance models.

We introduce a new method to calculate the credit exposure of Bermudan, discretely monitored barrier and European options. Core of the approach is the application of the dynamic Chebyshev method of Glau et al. (2019). The dynamic Chebyshev method delivers a closed form approximation of the option prices along the paths…

Bayesian optimization on networks using Gaussian process models.