Statistics Colloquium : Dr. Yaakov Malinovsky
UMBC
Friday, September 6, 2019 · 11 AM - 12 PM
Title:
A Unified Approach for Solving Sequential Selection Problems.
Abstract:
In this work we develop a unified approach for solving a wide class of sequential selection problems. This class includes, but is not limited to, selection problems with no-information, rank-dependent rewards, and considers both fixed as well as random problem horizons. The proposed framework is based on a reduction of the original selection problem to one of optimal stopping for a sequence of judiciously constructed independent random variables. We demonstrate that our approach allows exact and efficient computation of optimal policies and various performance metrics thereof for a variety of sequential selection problems, several of which have not been solved to date.
Acknowledgement:
This is joint work with Alexander Goldenshluger from University of Haifa, Israel, and Assaf Zeevi from Columbia University, USA.
A Unified Approach for Solving Sequential Selection Problems.
Abstract:
In this work we develop a unified approach for solving a wide class of sequential selection problems. This class includes, but is not limited to, selection problems with no-information, rank-dependent rewards, and considers both fixed as well as random problem horizons. The proposed framework is based on a reduction of the original selection problem to one of optimal stopping for a sequence of judiciously constructed independent random variables. We demonstrate that our approach allows exact and efficient computation of optimal policies and various performance metrics thereof for a variety of sequential selection problems, several of which have not been solved to date.
Acknowledgement:
This is joint work with Alexander Goldenshluger from University of Haifa, Israel, and Assaf Zeevi from Columbia University, USA.
