Constrained Binary Decision Making

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00386209" target="_blank" >RIV/68407700:21230/24:00386209 - isvavai.cz</a>

Result on the web

<a href="https://papers.nips.cc/paper_files/paper/2024/hash/6ffc307731cd1d6784c35c6c2875c122-Abstract-Conference.html" target="_blank" >https://papers.nips.cc/paper_files/paper/2024/hash/6ffc307731cd1d6784c35c6c2875c122-Abstract-Conference.html</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Constrained Binary Decision Making

Original language description

Binary statistical decision making involves choosing between two states based on statistical evidence. The optimal decision strategy is typically formulated through a constrained optimization problem, where both the objective and constraints are expressed as integrals involving two Lebesgue measurable functions, one of which represents the strategy being optimized. In this work, we present a comprehensive formulation of the binary decision making problem and provide a detailed characterization of the optimal solution. Our framework encompasses a wide range of well-known and recently proposed decision making problems as specific cases. We demonstrate how our generic approach can be used to derive the optimal decision strategies for these diverse instances. Our results offer a robust mathematical tool that simplifies the process of solving both existing and novel formulations of binary decision making problems which are in the core of many Machine Learning algorithms.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Advances in Neural Information Processing Systems 37 (NeurIPS 2024)

ISBN

9798331314385

ISSN

1049-5258

e-ISSN

1049-5258

Number of pages

24

Pages from-to

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Publisher name

Neural Information Processing Systems Foundation, Inc.

Place of publication

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Event location

Vancouver

Event date

Dec 10, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001633268100203