Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Mo, Weibin"'
Modern complex datasets often consist of various sub-populations. To develop robust and generalizable methods in the presence of sub-population heterogeneity, it is important to guarantee a uniform learning performance instead of an average one. In m
Externí odkaz:
http://arxiv.org/abs/2405.01709
We consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline dataset consisting of the historically offered assortment set, customer
Externí odkaz:
http://arxiv.org/abs/2302.03821
Publikováno v:
Journal of the American Statistical Association, 116:534, 699-707 (2021)
We thank the opportunity offered by editors for this discussion and the discussants for their insightful comments and thoughtful contributions. We also want to congratulate Kallus (2020) for his inspiring work in improving the efficiency of policy le
Externí odkaz:
http://arxiv.org/abs/2110.08936
Autor:
Mo, Weibin, Liu, Yufeng
Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, researchers can search for the optimal individualized treatment r
Externí odkaz:
http://arxiv.org/abs/2109.02570
Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule (ITR) that m
Externí odkaz:
http://arxiv.org/abs/2006.15121
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
International Journal for Numerical & Analytical Methods in Geomechanics; Feb2024, Vol. 48 Issue 3, p701-726, 26p
Autor:
Mo, Weibin1 (AUTHOR), Liu, Yufeng2 (AUTHOR) yfliu@email.unc.edu
Publikováno v:
Journal of the Royal Statistical Society: Series B (Statistical Methodology). Apr2022, Vol. 84 Issue 2, p440-472. 33p.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Mo, Weibin
Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with covariates, treatment assignments and outcomes, one common goal is to find individualized decision rules that map the indivi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60111ed70fa292109493f3a00142c071