no code implementations • 24 Aug 2022 • Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu
The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature.
1 code implementation • 4 Feb 2022 • Preetam Nandy, Xiufan Yu, Wanjun Liu, Ye Tu, Kinjal Basu, Shaunak Chatterjee
In this paper, we propose a generalization of tree-based approaches to tackle multiple discrete and continuous-valued treatments.
no code implementations • 4 Feb 2022 • Yiping Yuan, Ajith Muralidharan, Preetam Nandy, Miao Cheng, Prakruthi Prabhakar
Mobile notification systems have taken a major role in driving and maintaining user engagement for online platforms.
no code implementations • 19 Jun 2020 • Preetam Nandy, Cyrus DiCiccio, Divya Venugopalan, Heloise Logan, Kinjal Basu, Noureddine El Karoui
Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society.
no code implementations • 8 Jun 2019 • Kinjal Basu, Preetam Nandy
In this paper, we focus on the problem of stochastic optimization where the objective function can be written as an expectation function over a closed convex set.
1 code implementation • 29 Jan 2019 • Ye Tu, Kinjal Basu, Cyrus DiCiccio, Romil Bansal, Preetam Nandy, Padmini Jaikumar, Shaunak Chatterjee
In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization.
1 code implementation • 27 Sep 2018 • Abhishek Chakrabortty, Preetam Nandy, Hongzhe Li
In particular, we assume that the causal structure of the treatment, the confounders, the potential mediators and the response is a (possibly unknown) directed acyclic graph (DAG).