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Paper accepted at ACM FaccT 2024! – Machine Learning
The paper "CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale", authored by our members Ayan Majumdar and Isabel Valera, was accepted to ACM FAccT 2024! Link to the conference: https://facctconference.org/2024/ Paper DOI: https://doi.org/10.1145/3630106.3659003
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Paper accepted at ACM FaccT 2024! – Machine Learning
The paper "CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale", authored by our members Ayan Majumdar and Isabel Valera, was accepted to ACM FAccT 2024! Link to the conference: https://facctconference.org/2024/ Paper DOI: https://doi.org/10.1145/3630106.3659003
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https://machinelearning.uni-saarland.de/2024/04/paper-accepted-at-acm-facct-2024Paper accepted at ACM FaccT 2024! – Machine Learning
The paper "CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale", authored by our members Ayan Majumdar and Isabel Valera, was accepted to ACM FAccT 2024! Link to the conference: https://facctconference.org/2024/ Paper DOI: https://doi.org/10.1145/3630106.3659003
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- descriptionThe paper "CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale", authored by our members Ayan Majumdar and Isabel Valera, was accepted to ACM FAccT 2024! Link to the conference: https://facctconference.org/2024/ Paper DOI: https://doi.org/10.1145/3630106.3659003
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- og:descriptionThe paper "CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale", authored by our members Ayan Majumdar and Isabel Valera, was accepted to ACM FAccT 2024! Link to the conference: https://facctconference.org/2024/ Paper DOI: https://doi.org/10.1145/3630106.3659003
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