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Creators/Authors contains: "Jialu Wang"

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  1. ; ; (, International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  2. ; ; (, International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  3. ; ; (, Proceedings of Annual Meeting of Association for Computational Linguistics)
    Accepted Manuscript Full Text Available
  4. ; (, Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  5. (, Algorithmic Recourse workshop at ICML'21)
    null (Ed.)
    Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome. In such settings, strategic behavior leads to a sharp loss in model performance in deployment. In this work, we aim to address this problem by learning classifiers that encourage decision subjects to change their features in a way that leads to improvement in both predicted \emph{and} true outcome. We frame the dynamics of prediction and adaptation as a two-stage game, and characterize optimal strategies for the model designer and its decision subjects. In benchmarks on simulated and real-world datasets, we find that classifiers trained using our method maintain the accuracy of existing approaches while inducing higher levels of improvement and less manipulation. 
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    Accepted Manuscript Full Text Available
  6. ; ; (, Empirical Methods in Natural Language Processing)
    Accepted Manuscript Full Text Available