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University of Michigan
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Counterfactual fairness in reinforcement learning can be achieved with a new preprocessing algorithm that ensures equitable access in high-stakes decision-making.
Framing neural networks as a natural extension of linear regression could revolutionize how classical statisticians engage with modern predictive modeling techniques.
