ArticleScience advances2020
Learning to learn from data: Using deep adversarial learning to construct optimal statistical procedures.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Magnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learning.Science advances · 2023Article
- Adversarial meta-learning of Gamma-minimax estimators that leverage prior knowledge.Electronic journal of statistics · 2023Article
- Using game theory to thwart multistage privacy intrusions when sharing data.Science advances · 2021Article
- Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures.Journal of machine learning research : JMLR · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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 players' 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 statistician'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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.