Inverse-Weighted Survival Games
Year: 2021
Type: article
Abstract: . In these games, objectives for each model are built from re-weighted estimates featuring the other model, where the latter is held fixed during training. When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point. This means models in the game do not leave the correct distributions once reached. We construct one case where this stationary point is unique. We show that these games optimize BS on simulations and then apply these principles on real world cancer and critically-ill patient data.
Source: PubMed
Authors: Han, Xintian, Goldstein, Mark, Puli, Aahlad, Wies, Thomas, Perotte, Adler J, Ranganath, Rajesh
Institutions: New York University, Columbia University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Deep probabilistic predictive models for stroke and coronary heart disease, SHF: Small:Verifying Complex Concurrent Data Structures with Flow Interfaces, G3329785188, TWC: Medium: Scaling proof-based verifiable computation, NRT-HDR: FUTURE Foundations, Translation, and Responsibility for Data Science Impact
OpenAlex ID: W3213896168