Journal article
Learning-Augmented Robust Algorithmic Recourse
Transactions on Machine Learning Research, v 2026-
2026
Abstract
Algorithmic recourse provides individuals who receive undesirable outcomes from machine learning systems with minimum-cost improvements to achieve a desirable outcome. However, machine learning models often get updated, so the recourse may not lead to the desired outcome. The robust recourse framework chooses recourses that are less sensitive to adversarial model changes, but this comes at a higher cost. To address this, we initiate the study of learning-augmented algorithmic recourse and evaluate the extent to which a designer equipped with a prediction of the future model can reduce the cost of recourse when the prediction is accurate (consistency) while also limiting the cost even when the prediction is inaccurate (robustness). We propose a novel algorithm, study the robustness-consistency trade-off, and analyze how prediction accuracy affects performance.
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Details
- Title
- Learning-Augmented Robust Algorithmic Recourse
- Creators
- Kshitij Kayastha - Drexel University, College of Computing and InformaticsVasilis Gkatzelis - Drexel University, Computer ScienceShahin Jabbari - Drexel University, Computer Science
- Publication Details
- Transactions on Machine Learning Research, v 2026-
- Grant note
- CCF-2047907 / National Science Foundation (100000001) CCF-2210502 / National Science Foundation (100000001)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science; College of Computing and Informatics
- Scopus ID
- 2-s2.0-105037388888
- Other Identifier
- 991022189172804721