Poster
in
Workshop: Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Paula Rodriguez-Diaz · Kai Wang · David Alvarez-Melis · Milind Tambe
Abstract:
Optimal transport-based dataset distances are a principled way to measure task similarity, informing tasks like domain adaptation and transfer learning, typically assessed by prediction error minimization. However, in Predict-then-Optimize (PtO) frameworks, success is measured by decision regret minimization. We show that feature- and label-based distances lack informativeness in PtO and propose a new decision-aware distance that effectively captures adaptation success in PtO.
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