Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Stevens, Abby"'
Autor:
Fadikar, Arindam, Stevens, Abby, Collier, Nicholson, Toh, Kok Ben, Morozova, Olga, Hotton, Anna, Clark, Jared, Higdon, David, Ozik, Jonathan
Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, particularly in real-time or online environments where data arrives sequen
Externí odkaz:
http://arxiv.org/abs/2402.15619
The NSF-funded Robust Epidemic Surveillance and Modeling (RESUME) project successfully convened a workshop entitled "High-performance computing and large-scale data management in service of epidemiological modeling" at the University of Chicago on Ma
Externí odkaz:
http://arxiv.org/abs/2308.04602
Autor:
Fadikar, Arindam, Binois, Mickael, Collier, Nicholson, Stevens, Abby, Toh, Kok Ben, Ozik, Jonathan
Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration changes in case of a stochastic model since output from such a model is gene
Externí odkaz:
http://arxiv.org/abs/2305.03926
Autor:
Collier, Nicholson, Wozniak, Justin M., Stevens, Abby, Babuji, Yadu, Binois, Mickaël, Fadikar, Arindam, Würth, Alexandra, Chard, Kyle, Ozik, Jonathan
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among domain experts, mathematical modelers, and scientific computing specialists. Comput
Externí odkaz:
http://arxiv.org/abs/2304.14244
As opaque predictive models increasingly impact many areas of modern life, interest in quantifying the importance of a given input variable for making a specific prediction has grown. Recently, there has been a proliferation of model-agnostic methods
Externí odkaz:
http://arxiv.org/abs/2207.09097
Autor:
Saleiro, Pedro, Kuester, Benedict, Hinkson, Loren, London, Jesse, Stevens, Abby, Anisfeld, Ari, Rodolfa, Kit T., Ghani, Rayid
Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other possible characteristics. While a lot of bias metrics and fairness de
Externí odkaz:
http://arxiv.org/abs/1811.05577
Autor:
Hotton, Anna L., Ozik, Jonathan, Kaligotla, Chaitanya, Collier, Nick, Stevens, Abby, Khanna, Aditya S., MacDonell, Margaret M., Wang, Cheng, LePoire, David J., Chang, Young-Soo, Martinez-Moyano, Ignacio J., Mucenic, Bogdan, Pollack, Harold A., Schneider, John A., Macal, Charles
Publikováno v:
In Annals of Epidemiology December 2022 76:165-173
Autor:
Stevens, Abby, Willett, Rebecca, Mamalakis, Antonios, Foufoula-Georgiou, Efi, Tejedor, Alejandro, Randerson, James T., Smyth, Padhraic, Wright, Stephen
Publikováno v:
Journal of Climate, 2021 Jan . 34(2), 737-754.
Externí odkaz:
https://www.jstor.org/stable/27076142
Publikováno v:
In Genetics in Medicine Open 2023 1(1) Supplement
Autor:
Stevens, Abby
New statistical and machine learning methods have led to important advances in image and natural language processing, genetics, digital advertising, and other fields where there is an abundance of high-quality digital data and strong market incentive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dec5b910cefe3d93d710338124dabc4c