Project: Rational decision making under uncertainty: a new paradigm based on choice functions.
2019-01-01 – 2025-12-31
- Abstract
This project aims to develop a mathematical framework for rational decision making under uncertainty, and to design reliable classification methods with it. One of its key features is that it does away with the traditional yet unrealistic assumption that there is always an optimal choice.
Instead, we allow for indecision by using choice functions, and use this to our advantage.
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Conservative decision-making with sets of probabilities : how to infer new choices from previous ones
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- Conference Paper
- P1
- open access
Robustness quantification : a new method for assessing the reliability of the predictions of a classifier
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- Journal Article
- A1
- open access
The logic behind desirable sets of things, and its filter representation
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- Conference Paper
- P1
- open access
Desirable sets of things and their logic
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A theory of desirable things
(2023) Proceedings of Machine Learning Research. In Proceedings of Machine Learning Research 215. p.141-152 -
- Conference Paper
- C1
- open access
Inference with choice functions made practical
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- Journal Article
- A1
- open access
A robust dynamic classifier selection approach for hyperspectral images with imprecise label information
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Archimedean choice functions : an axiomatic foundation for imprecise decision making