论文标题
从预测到决策：使用lookahead正则化
From Predictions to Decisions: Using Lookahead Regularization
论文作者
论文摘要
机器学习是预测与人类相关结果的强大工具，从信用评分到心脏病发作风险。但是，当部署时，学习的模型还会影响用户的行为方式，以改善预测或真实的结果。学习的标准方法不可征用用户行动，并且对行动的影响没有保证。我们为学习既准确又促进良好行动的学习预测指标提供了一个框架。为此，我们介绍了Look-Ad-Ad-Ad-Adap Adartization，通过预测用户行动，鼓励预测模型也诱导改善结果的行动。该正则化仔细调整不确定性估计了对模型引起的动作分布的这种改善的信心。我们报告了关于实际和合成数据的实验结果，这些结果显示了这种方法的有效性。
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 regularization 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.
