9 citations · 47 across the 12 of their papers we have counts for
16 papers
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Brian Hsu, Rahul Mazumder, Preetam Nandy +1
The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature. It states that outside of special cases, one cannot exactly and simultaneousl…
Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity
Cyrus DiCiccio, Brian Hsu, YinYin Yu +2
Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected…
Long-term Dynamics of Fairness Intervention in Connection Recommender Systems
Nil-Jana Akpinar, Cyrus DiCiccio, Preetam Nandy +1
Recommender system fairness has been studied from the perspectives of a variety of stakeholders including content producers, the content itself and recipients of recommendations. R…
Offline Reinforcement Learning for Mobile Notifications
Yiping Yuan, Ajith Muralidharan, Preetam Nandy +2
Mobile notification systems have taken a major role in driving and maintaining user engagement for online platforms. They are interesting recommender systems to machine learning pr…
Generalized Causal Tree for Uplift Modeling
Preetam Nandy, Xiufan Yu, Wanjun Liu +3
Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment alloca…
A/B Testing for Recommender Systems in a Two-sided Marketplace
Preetam Nandy, Divya Venugopalan, Chun Lo +1
Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on…