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pro vyhledávání: '"Huang, Catherine"'
Predictive machine learning models are becoming increasingly deployed in high-stakes contexts involving sensitive personal data; in these contexts, there is a trade-off between model explainability and data privacy. In this work, we push the boundari
Externí odkaz:
http://arxiv.org/abs/2407.17663
Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage
Machine learning models are increasingly utilized across impactful domains to predict individual outcomes. As such, many models provide algorithmic recourse to individuals who receive negative outcomes. However, recourse can be leveraged by adversari
Externí odkaz:
http://arxiv.org/abs/2308.04341
Meta-reinforcement learning (RL) methods can meta-train policies that adapt to new tasks with orders of magnitude less data than standard RL, but meta-training itself is costly and time-consuming. If we can meta-train on offline data, then we can reu
Externí odkaz:
http://arxiv.org/abs/2107.03974
Autor:
Huang, Catherine
Objective: This scoping review will assess the nature and extent of the literature that describes and provides evidence for the use of digital health interventions (DHI) for outcome assessment in adult patients following a critical care admission. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::26f844cbfba3dca6b720c1353cab9103
Akademický článek
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This study investigated whether simulated material imitating the appearance of wood yields the same restorative benefits as its authentic counterpart (material made from genuine wood). Upon viewing images of classrooms containing either authentic woo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c6943ede6f51f3a691209342b4f21f5
Publikováno v:
Philosophical Transactions: Biological Sciences, 2005 Mar . 360(1455), 537-542.
Externí odkaz:
https://www.jstor.org/stable/30041279
Akademický článek
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Publikováno v:
In Biomaterials 2008 29(27):3671-3682