Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Wilder, Bryan"'
Autor:
Cortes-Gomez, Santiago, Patiño, Carlos, Byun, Yewon, Wu, Steven, Horvitz, Eric, Wilder, Bryan
There is increasing interest in ''decision-focused'' machine learning methods which train models to account for how their predictions are used in downstream optimization problems. Doing so can often improve performance on subsequent decision problems
Externí odkaz:
http://arxiv.org/abs/2410.01767
Autor:
Wilder, Bryan, Welle, Pim
Many social programs attempt to allocate scarce resources to people with the greatest need. Indeed, public services increasingly use algorithmic risk assessments motivated by this goal. However, targeting the highest-need recipients often conflicts w
Externí odkaz:
http://arxiv.org/abs/2407.07596
Distribution shift is a key challenge for predictive models in practice, creating the need to identify potentially harmful shifts in advance of deployment. Existing work typically defines these worst-case shifts as ones that most degrade the individu
Externí odkaz:
http://arxiv.org/abs/2407.03557
Estimates of causal parameters such as conditional average treatment effects and conditional quantile treatment effects play an important role in real-world decision making. Given this importance, one should ensure these estimators are calibrated. Wh
Externí odkaz:
http://arxiv.org/abs/2406.01933
Autor:
Dasgupta, Arpan, Boehmer, Niclas, Madhiwalla, Neha, Hedge, Aparna, Wilder, Bryan, Tambe, Milind, Taneja, Aparna
Automated voice calls are an effective method of delivering maternal and child health information to mothers in underserved communities. One method to fight dwindling listenership is through an intervention in which health workers make live service c
Externí odkaz:
http://arxiv.org/abs/2407.11973
The presence of inequity is a fundamental problem in the outcomes of decision-making systems, especially when human lives are at stake. Yet, estimating notions of unfairness or inequity is difficult, particularly if they rely on hard-to-measure conce
Externí odkaz:
http://arxiv.org/abs/2403.14713
Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, they can only examine a few of the thousands of maximally-tied outliers
Externí odkaz:
http://arxiv.org/abs/2401.01459
Epidemic surveillance is a challenging task, especially when crucial data is fragmented across institutions and data custodians are unable or unwilling to share it. This study aims to explore the feasibility of a simple federated surveillance approac
Externí odkaz:
http://arxiv.org/abs/2307.02616
Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the most important, outlying data points from thousands of daily-updated pub
Externí odkaz:
http://arxiv.org/abs/2306.16914
Large-scale datasets are increasingly being used to inform decision making. While this effort aims to ground policy in real-world evidence, challenges have arisen as selection bias and other forms of distribution shifts often plague observational dat
Externí odkaz:
http://arxiv.org/abs/2306.03302