Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insig…

Fast algorithms for matrix multiplication, namely those that perform asymptotically fewer scalar operations than the classical algorithm, have been c…

Motivated by the broadcast view of the interference channel, the new problem of communication with disturbance constraints is formulated. The rate-di…

The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with expos…

Heterogeneity is often natural in many contemporary applications involving massive data. While posing new challenges to effective learning, it can pl…

A general framework is developed to investigate the properties of useful choices of stationary spacelike slicings of stationary spacetimes whose cong…

We consider an intermediary's problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. More specificall…

This article presents explicit exponential integrators for stochastic Maxwell's equations driven by both multiplicative and additive noises. By utili…

This article provides a Wilsonian description of the perturbatively renormalizable Tensorial Group Field Theory introduced in arXiv:1303.6772 [hep-th…

We establish error bounds of the Lie-Trotter splitting ($S_1$) and Strang splitting ($S_2$) for the Dirac equation in the nonrelativistic limit regim…

For the large-scale linear discrete ill-posed problem $\min\|Ax-b\|$ or $Ax=b$ with $b$ contaminated by a white noise, Lanczos bidiagonalization base…

We introduce a very general method for high-dimensional classification, based on careful combination of the results of applying an arbitrary base cla…

For unconstrained control problems, a local convergence rate is established for an $hp$-method based on collocation at the Radau quadrature points in…

Any performance analysis based on stochastic simulation is subject to the errors inherent in misspecifying the modeling assumptions, particularly the…

Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative recon…

We propose empirical dynamic programming algorithms for Markov decision processes (MDPs). In these algorithms, the exact expectation in the Bellman o…

In this paper we consider large-scale smooth optimization problems with multiple linear coupled constraints. Due to the non-separability of the const…

We investigate a quasicontinuum method by means of analytical tools. More precisely, we compare a discrete-to-continuum analysis of an atomistic one-…

The traditional difficulty about stochastic singular control is to characterize the regularities of the value function and the optimal control policy…

We study a turbulence closure model in which the fractional Laplacian $(-\Delta)^\alpha$ of the velocity field represents the turbulence diffusivity.…

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