PT - JOURNAL ARTICLE
AU - Luedtke, Alex
AU - Carone, Marco
AU - Simon, Noah
AU - Sofrygin, Oleg
TI - Learning to learn from data: Using deep adversarial learning to construct optimal statistical procedures
AID - 10.1126/sciadv.aaw2140
DP - 2020 Feb 01
TA - Science Advances
PG - eaaw2140
VI - 6
IP - 9
4099 - http://advances.sciencemag.org/content/6/9/eaaw2140.short
4100 - http://advances.sciencemag.org/content/6/9/eaaw2140.full
SO - Sci Adv2020 Feb 01; 6
AB - Traditionally, statistical procedures have been derived via analytic calculations whose validity often relies on sample size growing to infinity. We use tools from deep learning to develop a new approach, adversarial Monte Carlo meta-learning, for constructing optimal statistical procedures. Statistical problems are framed as two-player games in which Nature adversarially selects a distribution that makes it difficult for a statistician to answer the scientific question using data drawn from this distribution. The playersâ€™ strategies are parameterized via neural networks, and optimal play is learned by modifying the network weights over many repetitions of the game. Given sufficient computing time, the statisticianâ€™s strategy is (nearly) optimal at the finite observed sample size, rather than in the hypothetical scenario where sample size grows to infinity. In numerical experiments and data examples, this approach performs favorably compared to standard practice in point estimation, individual-level predictions, and interval estimation.