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  A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems

  Authors: Mohammad-Amin Charusaie, Samira Samadi

  Article link: https://proceedings.neurips.cc/paper_files/paper/2024/file/2a5a41a536d3ada8fbf61a9d6fbf18d2-Paper-Conference.pdf

  Abstract: Learn-to-Defer is a paradigm that enables learning algorithms to work not in isolation but as a team with human experts. In this paradigm, we permit the system to defer a subset of its tasks to the expert. Although there are currently systems that follow this paradigm and are designed to optimize the accuracy of the final human-AI team, the general methodology for developing such systems under a set of constraints (e.g., algorithmic fairness, expert intervention budget, defer of anomaly, etc.) remains largely unexplored. In this paper, using a d-dimensional generalization to the fundamental lemma of Neyman and Pearson (d-GNP), we obtain the Bayes optimal solution for learn-to-defer systems under various constraints. Furthermore, we design a generalizable algorithm to estimate that solution and apply this algorithm to the COMPAS and ACSIncome datasets. Our algorithm shows improvements in terms of constraint violation over a set of baselines.

  Presenter: Mohammad-Amin Charusaie

  Date: 19 February 2026 at 10:00 CET

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