no code implementations • 21 Jul 2023 • Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany, Jilei Yang, Yuliia Lut, Rahul Tandra, Xiaowen Zhang, Xinyi Zheng, Zach Douglas, Vidita Nolkha, Parvez Ahammad, Gennady Samorodnitsky
We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies.
no code implementations • 16 Sep 2022 • Diana M. Negoescu, Pasha Khosravi, Shadow Zhao, Nanyu Chen, Parvez Ahammad, Humberto Gonzalez
This opens questions regarding not only which decision-making policies would perform best in practice, but also regarding the impact of different data collection protocols on the performance of various policies trained on the data, or the robustness of policy performance with respect to changes in problem characteristics such as action- or reward- specific delays in observing outcomes.
1 code implementation • 15 Jul 2022 • Reza Hosseini, Albert Chen, Kaixu Yang, Sayan Patra, Yi Su, Saad Eddin Al Orjany, Sishi Tang, Parvez Ahammad
We present Greykite, an open-source Python library for forecasting that has been deployed on over twenty use cases at LinkedIn.
no code implementations • 27 May 2021 • Jilei Yang, Diana Negoescu, Parvez Ahammad
Predictive machine learning models often lack interpretability, resulting in low trust from model end users despite having high predictive performance.
no code implementations • 26 Oct 2018 • Qingzhu Gao, Humberto Gonzalez, Parvez Ahammad
Development of interpretable machine learning models for clinical healthcare applications has the potential of changing the way we understand, treat, and ultimately cure, diseases and disorders in many areas of medicine.
no code implementations • 10 Nov 2016 • Heju Jiang, Jasvir Nagra, Parvez Ahammad
In this paper, we systematically study the methods, algorithms, and system designs in academic publications from 2008-2015 that applied ML in security domains.