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From Predictions to Decisions: Using Lookahead Regularization
[article]
<span title="2020-06-23">2020</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2020-06-20">2020</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.11638v1">arXiv:2006.11638v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hhtd4eo6w5anvmnj5qntreigja">fatcat:hhtd4eo6w5anvmnj5qntreigja</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.11638v1">arXiv:2006.11638v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hhtd4eo6w5anvmnj5qntreigja">fatcat:hhtd4eo6w5anvmnj5qntreigja</a> </span>
Machine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks. But when deployed, learned models also affect how users act in order to improve outcomes, whether predicted or real. The standard approach to learning is agnostic to induced user actions and provides no guarantees as to the effect of actions. We provide a framework for learning predictors that are both accurate and promote good actions. For this, we introduce look-ahead
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.11638v2">arXiv:2006.11638v2</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gcnmhr5r4nf63pgjzu2fdg4o3u">fatcat:gcnmhr5r4nf63pgjzu2fdg4o3u</a>
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... tion which, by anticipating user actions, encourages predictive models to also induce actions that improve outcomes. This regularization carefully tailors the uncertainty estimates governing confidence in this improvement to the distribution of model-induced actions. We report the results of experiments on real and synthetic data that show the effectiveness of this approach.
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