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Journal Articles
- 2013
[j1]Adi L. Tarca, Mario Lauria
, Michael Unger, Erhan Bilal, Stéphanie Boué, Kushal Kumar Dey, Julia Hoeng, Heinz Koeppl, Florian Martin, Pablo Meyer, Preetam Nandy, Raquel Norel, Manuel C. Peitsch, John Jeremy Rice, Roberto Romero, Gustavo Stolovitzky, Marja Talikka, Yang Xiang, Christoph Zechner, Improver Dsc Collaborators:
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge. Bioinform. 29(22): 2892-2899 (2013)
Conference and Workshop Papers
- 2023
- [c11]Preetam Nandy, Xiufan Yu, Wanjun Liu, Ye Tu, Kinjal Basu, Shaunak Chatterjee:
Generalized Causal Tree for Uplift Modeling. IEEE Big Data 2023: 788-798 - [c10]Cyrus DiCiccio, Brian Hsu, YinYin Yu, Preetam Nandy, Kinjal Basu:
Detection and Mitigation of Algorithmic Bias via Predictive Parity. FAccT 2023: 1801-1816 - 2022
- [c9]Nil-Jana Akpinar, Cyrus DiCiccio, Preetam Nandy, Kinjal Basu:
Long-term Dynamics of Fairness Intervention in Connection Recommender Systems. AIES 2022: 22-35 - [c8]Yiping Yuan, Ajith Muralidharan, Preetam Nandy, Miao Cheng, Prakruthi Prabhakar:
Offline Reinforcement Learning for Mobile Notifications. CIKM 2022: 3614-3623 - [c7]Preetam Nandy, Cyrus DiCiccio, Divya Venugopalan, Heloise Logan, Kinjal Basu, Noureddine El Karoui:
Achieving Fairness via Post-Processing in Web-Scale Recommender Systems✱. FAccT 2022: 715-725 - [c6]Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu:
Pushing the limits of fairness impossibility: Who's the fairest of them all? NeurIPS 2022 - 2021
- [c5]Preetam Nandy, Divya Venugopalan, Chun Lo, Shaunak Chatterjee:
A/B Testing for Recommender Systems in a Two-sided Marketplace. NeurIPS 2021: 6466-6477 - [c4]Ye Tu, Kinjal Basu, Cyrus DiCiccio, Romil Bansal, Preetam Nandy, Padmini Jaikumar, Shaunak Chatterjee:
Personalized Treatment Selection using Causal Heterogeneity. WWW 2021: 1574-1585 - 2020
- [c3]Preetam Nandy, Kinjal Basu, Shaunak Chatterjee, Ye Tu:
A/B Testing in Dense Large-Scale Networks: Design and Inference. NeurIPS 2020 - 2017
- [c2]Marco Eigenmann, Preetam Nandy, Marloes H. Maathuis:
Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges. UAI 2017 - 2012
- [c1]Christoph Zechner, Preetam Nandy, Michael Unger, Heinz Koeppl:
Optimal variational perturbations for the inference of stochastic reaction dynamics. CDC 2012: 5336-5341
Parts in Books or Collections
- 2016
- [p1]Marloes H. Maathuis, Preetam Nandy:
A Review of Some Recent Advances in Causal Inference. Handbook of Big Data 2016: 387-407
Informal and Other Publications
- 2022
- [i5]Yiping Yuan, Ajith Muralidharan, Preetam Nandy, Miao Cheng, Prakruthi Prabhakar:
Offline Reinforcement Learning for Mobile Notifications. CoRR abs/2202.03867 (2022) - [i4]Nil-Jana Akpinar, Cyrus DiCiccio, Preetam Nandy, Kinjal Basu:
Long-term Dynamics of Fairness Intervention in Connection Recommender Systems. CoRR abs/2203.16432 (2022) - [i3]Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu:
Pushing the limits of fairness impossibility: Who's the fairest of them all? CoRR abs/2208.12606 (2022) - 2021
- [i2]Preetam Nandy, Divya Venugopalan, Chun Lo, Shaunak Chatterjee:
A/B Testing for Recommender Systems in a Two-sided Marketplace. CoRR abs/2106.00762 (2021) - 2020
- [i1]Preetam Nandy, Amir Sepehri, Kinjal Basu, Heloise Logan, Deepak Agarwal, Noureddine El Karoui:
Scalable Assessment and Mitigation Strategies for Fairness in Rankings. CoRR abs/2006.11350 (2020)
Coauthor Index
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