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Preetam Nandy
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Year
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge
AL Tarca, M Lauria, M Unger, E Bilal, S Boue, K Kumar Dey, J Hoeng, ...
Bioinformatics 29 (22), 2892-2899, 2013
1242013
High-dimensional consistency in score-based and hybrid structure learning
P Nandy, A Hauser, MH Maathuis
The Annals of Statistics 46 (6A), 3151-3183, 2018
1142018
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings
P Nandy, MH Maathuis, TS Richardson
The Annals of Statistics 45 (2), 647-674, 2017
632017
A Review of Some Recent Advances in Causal Inference
MH Maathuis, P Nandy
Handbook of Big Data, 387-407, 2016
402016
Robust causal structure learning with some hidden variables
B Frot, P Nandy, MH Maathuis
Journal of the Royal Statistical Society, Series B: Statistical Methodology …, 2019
27*2019
Large-sample theory for the Bergsma-Dassios sign covariance
P Nandy, L Weihs, M Drton
Electronic Journal of Statistics 10 (2), 2287-2311, 2016
212016
Structure learning of linear gaussian structural equation models with weak edges
MF Eigenmann, P Nandy, MH Maathuis
33rd Conference on Uncertainty in Artificial Intelligence, 2017
162017
Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Models
A Chakrabortty, P Nandy, H Li
arXiv preprint arXiv:1809.10652, 2018
112018
Package ‘pcalg’
M Kalisch, A Hauser, M Maechler, D Colombo, D Entner, P Hoyer, ...
JSS Journal of Statistical Software https://doi. org/10.18637/jss. v047. i11, 2022
10*2022
A/B Testing in Dense Large-Scale Networks: Design and Inference
P Nandy, K Basu, S Chatterjee, Y Tu
arXiv preprint arXiv:1901.10505, 2019
102019
Optimal variational perturbations for the inference of stochastic reaction dynamics
C Zechner, P Nandy, M Unger, H Koeppl
2012 IEEE 51st IEEE conference on decision and control (CDC), 5336-5341, 2012
102012
Optimal convergence for stochastic optimization with multiple expectation constraints
K Basu, P Nandy
arXiv preprint arXiv:1906.03401, 2019
92019
Achieving Fairness via Post-Processing in Web-Scale Recommender Systems
P Nandy, C Diciccio, D Venugopalan, H Logan, K Basu, NE Karoui
arXiv preprint arXiv:2006.11350, 2021
7*2021
Optimal perturbations for the identification of stochastic reaction dynamics
P Nandy, M Unger, C Zechner, H Koeppl
IFAC Proceedings Volumes 45 (16), 686-691, 2012
72012
Personalized Treatment Selection using Causal Heterogeneity
Y Tu, K Basu, C DiCiccio, R Bansal, P Nandy, P Jaikumar, S Chatterjee
arXiv preprint arXiv:1901.10550, 2020
6*2020
pcalg: Methods for graphical models and causal inference
M Kalisch, A Hauser, M Maechler, D Colombo, D Entner, P Hoyer, ...
R Package retrieved from https://CRAN. R-project. org/package= pcalg, 2021
52021
A/B Testing for Recommender Systems in a Two-sided Marketplace
P Nandy, D Venugopalan, C Lo, S Chatterjee
Advances in Neural Information Processing Systems 34, 6466-6477, 2021
42021
Learning diagnostic signatures from microarray data using L1-regularized logistic regression
P Nandy, M Unger, C Zechner, KK Dey, H Koeppl
Systems Biomedicine 1 (4), 240-246, 2013
42013
Offline Reinforcement Learning for Mobile Notifications
Y Yuan, A Muralidharan, P Nandy, M Cheng, P Prabhakar
arXiv preprint arXiv:2202.03867, 2022
22022
Predictive Rate Parity Testing and Mitigation
C DiCiccio, B Hsu, YY Yu, P Nandy, K Basu
arXiv preprint arXiv:2204.05947, 2022
12022
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