Preserve: Preference Learning under uncertainty

Funded by ANR Young Researcher Grant (JCJC)

PI:Sébastien Destercke

Heudiasyc Laboratory (CNRS - UTC)
57 Avenue de Landshut,
Université de Technologie de Compiègne,
BP 20529, 60205 Compiègne cedex, France.

Dates: Jan. 1st, 2019 - Dec. 31st, 2023.


Overview: Learning user preferences plays an essential role in many problems , from helping a decision maker to choose between a few complex alternatives, to helping a user to pick the best choice among thousands or millions of them. A common problem in preference learning is that collected preferences from users may be subject to strong uncertainties and imprecision, because users may not be completely sure about their preferences, or because preferences are only given over a very small subset of objects (e.g., pairwise preferences on a small set of pairs when considering objects on large combinatorial domains). Faithfully accounting for such uncertainties may be a difficult tasks, unless one is ready to make extra, sometimes hard to check assumptions (e.g., that the decision maker is probabilistic). Such extra assumption may lead to biased inferences, which in turn can result in unwanted or non-optimal decisions. The goal of Preserve is to investigate methods relying on rich uncertainty representations, that allow for a more accurate modelling of present uncertainties and deliver more robust inferences.