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Review

2019

Review

2019

Abstract Stochastic optimization is an umbrella term that includes over a dozen fragmented communities, using a patchwork of… Expand

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Highly Cited

2017

Highly Cited

2017

Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However… Expand

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Highly Cited

2016

Highly Cited

2016

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important… Expand

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Highly Cited

2013

Highly Cited

2013

Decomposition procedures for distributional analysis: a unified framework based on the Shapley value

Decomposition techniques are used in many fields of economics to help disentangle and quantify the impact of various causal… Expand

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Highly Cited

2009

Highly Cited

2009

High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than… Expand

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Highly Cited

2008

Highly Cited

2008

In this article, a conservative least-squares polynomial reconstruction operator is applied to the discontinuous Galerkin method… Expand

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Highly Cited

2006

Highly Cited

2006

We introduce and investigate a framework for constructing algorithms to invert Laplace transforms numerically. Given a Laplace… Expand

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Highly Cited

2003

Highly Cited

2003

This paper addresses the joint design of transmit and receive beamforming or linear processing (commonly termed linear precoding… Expand

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Highly Cited

2002

Highly Cited

2002

Search-theoretic models of monetary exchange are based on explicit descriptions of the frictions that make money essential… Expand

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Highly Cited

1994

Highly Cited

1994

We present a unified framework for the study of linear time-invariant (LTI) systems subject to control input nonlinearities. The… Expand

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