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A remarkable recent paper by Rubinfeld and Vasilyan (2022) initiated the study of testable learning, where the goal is to replace hard-to-verify distributional assumptions (such as Gaussianity) with efficiently testable ones and to require that the ...

We present a new sublinear time algorithm for approximating the spectral density (eigenvalue distribution) of an n× n normalized graph adjacency or Laplacian matrix. The algorithm recovers the spectrum up to є accuracy in the Wasserstein-1 distance in O(...

Many phenomena are modeled by deterministic differential equations, whereas the observation of these phenomena, in particular in life sciences, exhibits an important variability. This paper addresses the following question: how can the model be adapted to ...

In this paper, we introduce and study a two-stage distributionally robust mixed binary problem (TSDR-MBP) in which the random parameters follow the worst-case distribution belonging to an uncertainty set of probability distributions. We present a ...

We study the conjugacy approximation models in the context of Bayesian ranking and selection with unknown correlations. Under the assumption of normal-inverse-Wishart prior distribution, the posterior distribution remains a normal-inverse-Wishart ...

Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications. Although considerable research has been devoted to the development of rank aggregation algorithms, one basic question is how to efficiently collect a ...

A scenario tree is an efficient way to represent a stochastic data process in decision problems under uncertainty. This paper addresses how to efficiently generate appropriate scenario trees. A knowledge-based scenario tree generation method is proposed;...

We present and analyze a central cutting surface algorithm for general semi-infinite convex optimization problems and use it to develop a novel algorithm for distributionally robust optimization problems in which the uncertainty set consists of probability ...

<P>This paper considers time-dependent Ph_t/M_t/s/c queueing nodes and small tandem networks of such nodes. We examine characteristics of the departure processes from a multiserver queueing node; in particular, we focus on solving for the first two time-...

An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of ...

An advanced review of Response Modeling Methodology RMM has recently summarized RMM core philosophy, modeling approach, and allied statistical expressions. This focus article complements the earlier review by presenting a step-by-step guide to ...

We examine a new method of producing reduced order models for LTI systems which attempts to minimize a bound on the peak error between the original and reduced order models subject to a bound on the peak value of the input. The method, which can be ...

In wireless channels, multipath fading and shadowing occur simultaneously leading to the phenomenon referred to as composite fading. The use of the Nakagami probability density function (PDF) to model multipath fading and the Gamma PDF to model ...

Using the Nakagami probability density function (PDF) to model multipath fading and the Gamma PDF to model shadowing, in a wireless channel, has led to a closed-form expression for the composite fading PDF, known as the generalized-K PDF (also called ...

Slew rate determines the ability of a device to handle the varying signals. Determination of the slew rate to a good proximity is thus essential for efficient design of high speed CMOS integrated circuits. This in turn estimates the output switching ...

This paper presents a multiparameter moment-matching-based model order reduction technique for parameterized interconnect networks via a novel two-directional Arnoldi process (TAP). It is referred to as a Parameterized Interconnect Macromodeling via a ...

We present a multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame ...

As the power density increases exponentially, the runtime regulation of operating temperature by dynamic thermal management (DTM) becomes necessary. This paper proposes two novel approaches to the thermal analysis at the chip architecture level for ...

A rational Krylov algorithm for eigenvalue computation and model order reduction is described. It is shown how to implement it as a modified shift-and-invert spectral transformation Arnoldi decomposition. It is shown how to do deflation, locking ...