RT Journal Article
SR Electronic
T1 Learning to learn from data: Using deep adversarial learning to construct optimal statistical procedures
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP eaaw2140
DO 10.1126/sciadv.aaw2140
VO 6
IS 9
A1 Luedtke, Alex
A1 Carone, Marco
A1 Simon, Noah
A1 Sofrygin, Oleg
YR 2020
UL http://advances.sciencemag.org/content/6/9/eaaw2140.abstract
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.