Yaron Shaposhnik

University of Rochester - Simon Business School

Rochester, NY 14627

United States

SCHOLARLY PAPERS

9

DOWNLOADS
Rank 37,741

SSRN RANKINGS

Top 37,741

in Total Papers Downloads

1,833

SSRN CITATIONS

4

CROSSREF CITATIONS

0

Scholarly Papers (9)

1.

Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation

Number of pages: 43 Posted: 12 Jun 2019 Last Revised: 20 Jul 2022
Cynthia Rudin, Cynthia Rudin and Yaron Shaposhnik
Duke UniversityDuke University - Pratt School of Engineering and University of Rochester - Simon Business School
Downloads 747 (48,227)
Citation 1

Abstract:

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Explainable Artificial Intelligence (XAI), Local Explanations, Interpretability, Credit Risk

2.

Mining Optimal Policies: A Pattern Recognition Approach to Model Analysis

Number of pages: 44 Posted: 27 Nov 2017 Last Revised: 22 Aug 2018
Fernanda Bravo and Yaron Shaposhnik
University of California, Los Angeles (UCLA) - Anderson School of Management and University of Rochester - Simon Business School
Downloads 297 (144,301)

Abstract:

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Mathematical modeling, Machine learning, Dynamic programming, Interpretability

3.

Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs

Number of pages: 50 Posted: 15 May 2019 Last Revised: 07 Aug 2020
University of California, Los Angeles (UCLA) - Anderson School of Management, Duke UniversityDuke University - Pratt School of Engineering, University of Rochester - Simon Business School and University of Rochester - Simon Business School
Downloads 222 (192,317)

Abstract:

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Congestion; Queueing systems; Prediction; Service operation; Machine learning; Interpretability; ICU

4.

The Pandora's Box Problem with Sequential Inspections

Number of pages: 73 Posted: 13 Mar 2021 Last Revised: 17 Jun 2021
Ali Aouad, Jingwei Ji and Yaron Shaposhnik
London Business School, University of Southern California and University of Rochester - Simon Business School
Downloads 153 (266,161)

Abstract:

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pandora's box problem, dynamic programming, approximate algorithms

5.

Stochastic Selection Problems with Testing

Number of pages: 29 Posted: 11 Dec 2017
Chen Attias, Robert Krauthgamer, Retsef Levi and Yaron Shaposhnik
Weizmann Institute of Science, Weizmann Institute of Science, MIT Sloan School of Management - Operations Research Center and University of Rochester - Simon Business School
Downloads 129 (304,125)

Abstract:

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Selection, Testing, Dynamic programming, Approximation algorithms, Myopic policies

6.

An Adaptive SPT Rule for Scheduling and Testing Heterogeneous Jobs

Number of pages: 57 Posted: 13 Aug 2019
Retsef Levi, Thomas L. Magnanti and Yaron Shaposhnik
MIT Sloan School of Management - Operations Research Center, Massachusetts Institute of Technology (MIT) and University of Rochester - Simon Business School
Downloads 88 (393,282)
Citation 2

Abstract:

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Dynamic Programming, Maintenance, Diagnosis, Scheduling

7.

Waiting-Time Prediction with Invisible Customers

Number of pages: 43 Posted: 25 Oct 2021 Last Revised: 06 Nov 2021
Ben-Gurion University of the Negev, University of Rochester - Simon Business School, University of Toronto, University of Rochester - Simon Business School and University of Rochester - Simon Business School
Downloads 74 (434,722)

Abstract:

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waiting time prediction, queues, invisible customers, machine learning.

8.

A Polynomial-Time Approximation Scheme for Sequential Batch Testing of Series Systems

Number of pages: 32 Posted: 29 Nov 2018
Danny Segev and Yaron Shaposhnik
Tel Aviv University - School of Mathematical Sciences and University of Rochester - Simon Business School
Downloads 71 (444,483)
Citation 1

Abstract:

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sequential testing, approximation algorithms, PTAS, dynamic programming

9.

Visualizing The Implicit Model Selection Tradeoff

Number of pages: 49 Posted: 25 Oct 2021 Last Revised: 09 Mar 2022
Zezhen (Dawn) He and Yaron Shaposhnik
University of Rochester - Simon Business School and University of Rochester - Simon Business School
Downloads 52 (516,488)

Abstract:

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Model selection, Interpretability, Machine learning, Visualization