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Modeling Material Stress Using Integrated Gaussian Markov Random Fields
The equations of a physical constitutive model for material stress within tantalum grains were solved numerically using a tetrahedrally meshed volume. The resulting output included a scalar vonMises stress for each of the more t ... Read More >
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Multi-Armed Bandits with Correlated Arms
We consider a multi-armed bandit framework where the rewards obtained by pulling different arms are correlated. We develop a unified approach to leverage these reward correlations and present fundamental generalizations of class ... Read More >
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Modelling extreme claims via composite models and threshold selection methods
The existence of large and extreme claims of a non-life insurance portfolio influences the ability of (re)insurers to estimate the reserve. The excess over-threshold method provides a way to capture and model the typical behavio ... Read More >
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Iterative Estimation of Mixed Exponential Random Graph Models with Nodal Random ...
The presence of unobserved node specific heterogeneity in Exponential Random Graph Models (ERGM) is a general concern, both with respect to model validity as well as estimation instability. We therefore extend the ERGM by includ ... Read More >
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Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimate ...
In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and recently extended by Bu, Zou, and Veeravalli (2019). Our main contributions are significantl ... Read More >
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A Conway-Maxwell-Multinomial Distribution for Flexible Modeling of Clustered Cat ...
Categorical data are often observed as counts resulting from a fixed number of trials in which each trial consists of making one selection from a prespecified set of categories. The multinomial distribution serves as a standard ... Read More >
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Designing over uncertain outcomes with stochastic sampling Bayesian optimization
Optimization is becoming increasingly common in scientific and engineering domains. Oftentimes, these problems involve various levels of stochasticity or uncertainty in generating proposed solutions. Therefore, optimization in t ... Read More >
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Multiple penalized least squares and sign constraints with modified Newton-Raphs ...
Multiple penalized least squares (MPLS) models are a flexible approach to find adaptive least squares solutions required to be simultaneously sparse and smooth. This is particularly important when addressing real-life inverse pr ... Read More >
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GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Mo ...
A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs. An issue with this approach is choosin ... Read More >
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Neural Network Based Parameter Estimation Method for the Pareto/NBD Model
Whether stochastic or parametric, the Pareto/NBD model can only be utilized for an in-sample prediction rather than an out-of-sample prediction. This research thus provides a neural network based extension of the Pareto/NBD mode ... Read More >