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Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing…

Machine Learning · Computer Science 2022-11-04 Dennis Wei , Rahul Nair , Amit Dhurandhar , Kush R. Varshney , Elizabeth M. Daly , Moninder Singh

Statistical inference on the mean of a Poisson distribution is a fundamentally important problem with modern applications in, e.g., particle physics. The discreteness of the Poisson distribution makes this problem surprisingly challenging,…

Methodology · Statistics 2012-07-03 Ryan Martin , Duncan Ermini Leaf , Chuanhai Liu

Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing, and is typically only feasible using approximate MCMC sampling. In this article we propose a minimax tilting…

Computation · Statistics 2016-03-15 Z. I. Botev

Recently the regular conditional distributions of max-infinitely divisible processes were derived by \citet{Dombry2011} and although these conditional distributions have complicated closed forms, \citet{Dombry2011b} introduce an algorithm…

Statistics Theory · Mathematics 2012-08-28 Clément Dombry , Mathieu Ribatet

We address the notion of association of sum- and max- stable processes from the perspective of linear and max-linear isometries. We establish the appealing results that these two classes of isometries can be identified on a proper space…

Probability · Mathematics 2009-10-13 Yizao Wang , Stilian A. Stoev

Computer simulations that demonstrate the valueof novel approaches are crucial to developing more flexibleand robust power systems operations with high penetrations ofrenewable energy at multiple geographic and temporal scales.However,…

Systems and Control · Electrical Eng. & Systems 2020-09-01 Jose Daniel Lara , Jonathan T. Lee , Duncan Callaway , Bri-Mathias Hodge

Variational representations of divergences and distances between high-dimensional probability distributions offer significant theoretical insights and practical advantages in numerous research areas. Recently, they have gained popularity in…

Machine Learning · Computer Science 2022-03-25 Jeremiah Birrell , Markos A. Katsoulakis , Yannis Pantazis

In this paper, we propose an approach for computing invariant sets of discrete-time nonlinear systems by lifting the nonlinear dynamics into a higher dimensional linear model. In particular, we focus on the \emph{maximal admissible…

Systems and Control · Electrical Eng. & Systems 2022-07-22 Zheming Wang , Raphaël M. Jungers , Chong-Jin Ong

Generalized Brown-Resnick processes form a flexible class of stationary max-stable processes based on Gaussian random fields. With regard to applications fast and accurate simulation of these processes is an important issue. In fact,…

Probability · Mathematics 2010-09-30 Marco Oesting

We consider perfect simulation algorithms for locally stable point processes based on dominated coupling from the past, and apply these methods in two different contexts. A new version of the algorithm is developed which is feasible for…

Methodology · Statistics 2010-03-02 Graeme K. Ambler , Bernard W. Silverman

Piecewise regression is a versatile approach used in various disciplines to approximate complex functions from limited, potentially noisy data points. In control, piecewise regression is, e.g., used to approximate the optimal control law of…

Optimization and Control · Mathematics 2024-07-10 Dieter Teichrib , Moritz Schulze Darup

In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a $d$-dimensional domain. This method was proposed by Adams, Murray…

Statistics Theory · Mathematics 2015-03-03 Alisa Kirichenko , Harry van Zanten

Multiphysics incompressible fluid dynamics simulations play a crucial role in understanding intricate behaviors of many complex engineering systems that involve interactions between solids, fluids, and various phases like liquid and gas.…

Computational Engineering, Finance, and Science · Computer Science 2023-12-27 Akash Dhruv

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control…

Real-time simulation of elastic structures is essential in many applications, from computer-guided surgical interventions to interactive design in mechanical engineering. The Finite Element Method is often used as the numerical method of…

Machine Learning · Computer Science 2021-09-21 Alban Odot , Ryadh Haferssas , Stéphane Cotin

A new class of stochastic processes called independent and periodically identically distributed (i.p.i.d.) processes is defined to capture periodically varying statistical behavior. Algorithms are proposed to detect changes in such i.p.i.d.…

Statistics Theory · Mathematics 2018-10-31 Taposh Banerjee , Prudhvi Gurram , Gene Whipps

This study introduces a novel point-wise diffusion model that processes spatio-temporal points independently to efficiently predict complex physical systems with shape variations. This methodological contribution lies in applying forward…

Computational Physics · Physics 2025-08-05 Jiyong Kim , Sunwoong Yang , Namwoo Kang

The diffusion bridge, which is a diffusion process conditioned on hitting a specific state within a finite period, has found broad applications in various scientific and engineering fields. However, simulating diffusion bridges for modeling…

Machine Learning · Computer Science 2025-05-02 Gefan Yang , Elizabeth Louise Baker , Michael L. Severinsen , Christy Anna Hipsley , Stefan Sommer

This article presents an empirical validation of the functional multidimensional scaling model, a novel approach that improves the smoothness of time-varying dissimilarities in a low-dimensional space, embedding a modified Adam stochastic…

Applications · Statistics 2025-05-21 Liting Li

The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general…

Machine Learning · Statistics 2012-12-04 Xun Huan , Youssef M. Marzouk