Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Kristian Lum"'
Autor:
Eric T, Lofgren, Kristian, Lum, Aaron, Horowitz, Brooke, Mabubuonwu, Kellen, Meyers, Nina H, Fefferman
Publikováno v:
Epidemiology. 33:480-492
COVID-19 is challenging many societal institutions, including our criminal justice systems. Some have proposed or enacted (e.g., the State of New Jersey) reductions in the jail and/or prison populations. We present a mathematical model to explore the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c1d8d16a39ab8a75ac10d2a50edb7d8
https://doi.org/10.1097/ede.0000000000001476
https://doi.org/10.1097/ede.0000000000001476
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:
Significance. 18:30-32
Lessons from recent attempts to subvert the US election with data analysis. By Kristian Lum, Naim Kabir and Joe Bak-Coleman
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::897749ea5b8a9831192b5e9e138bf56d
https://doi.org/10.1111/1740-9713.01550
https://doi.org/10.1111/1740-9713.01550
Autor:
Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszár, Rumman Chowdhury
The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal biases. Most metrics attempting to quantify disparities resulting from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::296505fb0c8d02fac7dd7b7f7fb02f8a
http://arxiv.org/abs/2202.01615
http://arxiv.org/abs/2202.01615
When a model's performance differs across socially or culturally relevant groups--like race, gender, or the intersections of many such groups--it is often called "biased." While much of the work in algorithmic fairness over the last several years has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6df8302dd47cf225c5c294a7537b5f43
Publikováno v:
Biometrika. 106:197-210
SUMMARY Population estimation methods are used for estimating the size of a population from samples of individuals. In many applications, the probability of being observed in the sample varies across individuals, resulting in sampling bias. We show t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::51407273f412ae03a0821644d79057e3
https://doi.org/10.1093/biomet/asy065
https://doi.org/10.1093/biomet/asy065
Autor:
Kristian Lum, Frank Portman, Saikishore Kalloori, Wenzhe Shi, Michael M. Bronstein, Alykhan Tejani, Vito Walter Anelli, Jonathan Hunt, Luca Belli, Yuanpu Xie, Alexandre Lung-Yut-Fong, Bruce Ferwerda, Ben Chamberlain
After the success the RecSys 2020 Challenge, we are describing a novel and bigger dataset that was released in conjunction with the ACM RecSys Challenge 2021. This year's dataset is not only bigger (~ 1B data points, a 5 fold increase), but for the f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c735528f4e22d8efd67a74bb588aca8f
Publikováno v:
J R Stat Soc Ser A Stat Soc
In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::751429a8ca809f421ac54ad64f28011f
https://hdl.handle.net/11577/3470395
https://hdl.handle.net/11577/3470395
Publikováno v:
Harvard Data Science Review.
Knowledge of the number of individuals who have been infected with the novel coronavirus SARS-CoV-2 and the extent to which attempts for mitigation by executive order have been effective at limiting its spread are critical for effective policy going
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1aee2cdfe238ea2809955969236227a1
https://doi.org/10.1162/99608f92.7679a1ed
https://doi.org/10.1162/99608f92.7679a1ed
Autor:
Cody E. Cotner, Christian Terwiesch, Asaf Hanish, Gary E. Weissman, Barry D. Fuchs, Kristian Lum, James E. Johndrow, Olivia S. Jew, ThaiBinh Luong, Kevin G. Volpp, April Cardone, Ravi B. Parikh, Michael Draugelis
Due to the global shortage of PPE caused by increasing number of COVID-19 patients in recent months, many hospitals have had difficulty procuring adequate PPE for the clinicians who care for these patients. Faced with a shortage, hospitals have had t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd063cff6097b489ceef10ed4b76ef24