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pro vyhledávání: '"Rose, Evan A."'
As machine learning models become increasingly complex, concerns about their robustness and trustworthiness have become more pressing. A critical vulnerability of these models is data poisoning attacks, where adversaries deliberately alter training d
Externí odkaz:
http://arxiv.org/abs/2410.08872
Privacy-preserving machine learning (PPML) enables multiple data owners to contribute their data privately to a set of servers that run a secure multi-party computation (MPC) protocol to train a joint ML model. In these protocols, the input data rema
Externí odkaz:
http://arxiv.org/abs/2409.15126
Machine learning is susceptible to poisoning attacks, in which an attacker controls a small fraction of the training data and chooses that data with the goal of inducing some behavior unintended by the model developer in the trained model. We conside
Externí odkaz:
http://arxiv.org/abs/2311.11544
We develop an Empirical Bayes grading scheme that balances the informativeness of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the racial biases of 97 U.S.
Externí odkaz:
http://arxiv.org/abs/2306.13005
Autor:
Rose, Evan K., Shem-Tov, Yotam
Researchers using instrumental variables to investigate ordered treatments often recode treatment into an indicator for any exposure. We investigate this estimand under the assumption that the instruments shift compliers from no treatment to some but
Externí odkaz:
http://arxiv.org/abs/2111.12258
Akademický článek
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Akademický článek
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Publikováno v:
AEA Papers and Proceedings, 2021 May 01. 111, 43-48.
Externí odkaz:
https://www.jstor.org/stable/27042269
Autor:
Massenkoff, Maxim, Rose, Evan K.
Publikováno v:
American Economic Journal: Applied Economics; Oct2024, Vol. 16 Issue 4, p444-483, 40p
Autor:
Garin, Andrew, Koustas, Dmitri K., McPherson, Carl, Norris, Samuel, Pecenco, Matthew, Rose, Evan K., Shem-Tov, Yotam, Weaver, Jeffrey
Publikováno v:
NBER Working Papers; Jul2024, Issue 32620-32746, Preceding p1-48, 76p