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pro vyhledávání: '"Kyono, Trent"'
Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this is that s
Externí odkaz:
http://arxiv.org/abs/2202.02096
Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a regularization scheme that encourages any baseline imputation method t
Externí odkaz:
http://arxiv.org/abs/2111.03187
Machine learning models have been criticized for reflecting unfair biases in the training data. Instead of solving for this by introducing fair learning algorithms directly, we focus on generating fair synthetic data, such that any downstream learner
Externí odkaz:
http://arxiv.org/abs/2110.12884
Selecting causal inference models for estimating individualized treatment effects (ITE) from observational data presents a unique challenge since the counterfactual outcomes are never observed. The problem is challenged further in the unsupervised do
Externí odkaz:
http://arxiv.org/abs/2102.06271
Regularization improves generalization of supervised models to out-of-sample data. Prior works have shown that prediction in the causal direction (effect from cause) results in lower testing error than the anti-causal direction. However, existing reg
Externí odkaz:
http://arxiv.org/abs/2009.13180
Autor:
Kyono, Trent, van der Schaar, Mihaela
For decades, researchers in fields, such as the natural and social sciences, have been verifying causal relationships and investigating hypotheses that are now well-established or understood as truth. These causal mechanisms are properties of the nat
Externí odkaz:
http://arxiv.org/abs/1911.12441
With an aging and growing population, the number of women requiring either screening or symptomatic mammograms is increasing. To reduce the number of mammograms that need to be read by a radiologist while keeping the diagnostic accuracy the same or b
Externí odkaz:
http://arxiv.org/abs/1811.02661
Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is
Externí odkaz:
http://arxiv.org/abs/1807.05935
Publikováno v:
In Journal of the American College of Radiology January 2020 17(1) Part A:56-63
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