no code implementations • ICLR 2018 • Anton Nemchenko, Trent Kyono, Mihaela van der Schaar
Survival analysis in the presence of multiple possible adverse events, i. e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.).
no code implementations • 27 Nov 2019 • Trent Kyono, Mihaela van der Schaar
We show in this paper how prior knowledge in the form of a causal graph can be utilized to guide model selection, i. e., to identify from a set of trained networks the models that are the most robust and invariant to unseen domains.
1 code implementation • NeurIPS 2020 • Trent Kyono, Yao Zhang, Mihaela van der Schaar
Regularization improves generalization of supervised models to out-of-sample data.
no code implementations • 11 Feb 2021 • Trent Kyono, Ioana Bica, Zhaozhi Qian, Mihaela van der Schaar
We leverage the invariance of causal structures across domains to propose a novel model selection metric specifically designed for ITE methods under the UDA setting.
1 code implementation • NeurIPS 2021 • Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar
In this paper, we introduce DECAF: a GAN-based fair synthetic data generator for tabular data.
1 code implementation • NeurIPS 2021 • Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar
Missing data is an important problem in machine learning practice.
1 code implementation • 4 Feb 2022 • Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar
However, no imputation at all also leads to biased estimates, as missingness determined by treatment introduces bias in covariates.