Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Alexander D'Amour"'
Autor:
Harini Suresh, Divya Shanmugam, Tiffany Chen, Annie G Bryan, Alexander D'Amour, John Guttag, Arvind Satyanarayan
Publikováno v:
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c27b6df4d45adfa40e0caca9c6aca2f
https://doi.org/10.1145/3544548.3581482
https://doi.org/10.1145/3544548.3581482
Autor:
Alexander D'Amour, Emily B. Fox, Rif A. Saurous, Andrew Miller, Nicholas J. Foti, Joseph A Lewnard, Lauren Hannah, Joseph Futoma, Mark Sandler
Publikováno v:
Epidemiology (Cambridge, Mass.), vol 33, iss 4
Accurate measurement of daily infection incidence is crucial to epidemic response. However, delays in symptom onset, testing, and reporting obscure the dynamics of transmission, necessitating methods to remove the effects of stochastic delays from ob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec9a31d9893f62544ea4849e58b3dc1b
https://escholarship.org/uc/item/42s1f1h2
https://escholarship.org/uc/item/42s1f1h2
Publikováno v:
Annual Review of Statistics and Its Application. 8:141-163
A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of statistical and machine learning models. The rapid growt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1776e4836aae6ded98b9a48f86af7ae
https://doi.org/10.1146/annurev-statistics-042720-125902
https://doi.org/10.1146/annurev-statistics-042720-125902
Publikováno v:
Industrial and Corporate Change. 29:61-80
Following widespread availability of computerized databases, much research has correlated bibliometric measures from papers or patents to subsequent success, typically measured as the number of publications or citations. Building on this large body o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::212977088622acd6be71344068e1f777
https://doi.org/10.1093/icc/dtz042
https://doi.org/10.1093/icc/dtz042
Autor:
Mark Sandler, Shawn O'Banion, Luyang Liu, Rif A. Saurous, Xue Ben, Junpeng Lao, Sharad Vikram, Matthew D. Hoffman, Alexander D'Amour
In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19’s spread. Unfortunately, we cannot directly observe the prevalence o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ead6ec9f2333e816bb602d34fb954c8
Autor:
Hansa Srinivasan, James Atwood, Alexander D'Amour, Yoni Halpern, D. Sculley, Pallavi Baljekar
Publikováno v:
FAT*
As machine learning becomes increasingly incorporated within high impact decision ecosystems, there is a growing need to understand the long-term behaviors of deployed ML-based decision systems and their potential consequences. Most approaches to und
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c2785a606d565f2cff6708e02b2aa8b
https://doi.org/10.1145/3351095.3372878
https://doi.org/10.1145/3351095.3372878
Autor:
Sylvain Gelly, Mario Lucic, Matthias Minderer, Neil Houlsby, Alexander Kolesnikov, Rob Romijnders, Alexander D'Amour, Michael Tschannen, Josip Djolonga, Jessica Yung, Xiaohua Zhai, Joan Puigcerver, Dan Moldovan, Lucas Beyer
Publikováno v:
CVPR
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. However, several recent breakthroughs in transfer learning suggest that these networks can cope with severe distribution shifts and succe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73bd370d6b4715a06292f6817aa35a71
Autor:
Alexander D'Amour
The aim of this comment (set to appear in a formal discussion in JASA) is to draw out some conclusions from an extended back-and-forth I have had with Wang and Blei regarding the deconfounder method proposed in "The Blessings of Multiple Causes" [arX
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a3a2cbe8bd96217f47726956972b083
http://arxiv.org/abs/1910.08042
http://arxiv.org/abs/1910.08042
A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is therefore important in practice. Unfortunately, some existing sensitivity an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b558a1957f0fa8003c723b68dd78f40d
Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4edde30ae4762d4abfa6fcc5bf8e7d1f
http://arxiv.org/abs/1711.02582
http://arxiv.org/abs/1711.02582