Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From Predictions to Decisions: Using Lookahead Regularization   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.

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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg