Julien Chhor, Rajarshi Mukherjee, and Subhabrata Sen. 2023. “Sparse Signal Detection in Heteroscedastic Gaussian Sequence Models: Sharp Minimax Rates.” Bernoulli (To Appear). Arxiv

Nilanjana Laha, Nathan Huey, Brent Coull, and Rajarshi Mukherjee. 2023. “On Statistical Inference with High Dimensional Sparse CCA.” Information and Inference (To Appear). Arxiv

Yu-Jyun Huang, Rajarshi Mukherjee, and Chuhsing Kate Hsiao. 2022. “Probabilistic Edge Inference of Gene Networks with Bayesian Markov Random Field Modeling.” Frontiers in Genetics. bioRxiv

Nilanjana Laha and Rajarshi Mukherjee. 2022. “On Support Recovery With Sparse CCA: Information Theoretic and Computational Limits.” IEEE Transactions of Information Theory. Arxiv

Wenying Deng, Beau Cocker, Rajarshi Mukherjee, Jeremiah Zhe Liu, and Brent Coull. 2022. “Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees.” NeurIPS. Arxiv

Weeberb J. Requia, Heresh Amini, Rajarshi Mukherjee, Diane R. Gold, and Joel D. Schwartz. 2021. “Health impacts of wildfire-related air pollution in Brazil: A nationwide of more than 2 million hospital admissions between 2008 and 2018.” Nature Communications.

Chi-Hsuan Ho, Yu-Jyun Huang, Ying-Ju Lai, Rajarshi Mukherjee, and Chuhsing Kate Hsiao. 2021. “The misuse of distributional assumptions in functional class scoring gene-set and pathway analysis.” G3: Genes Genomes Genetics.

Lin Liu, Rajarshi Mukherjee, James M. Robins, and Eric Tchetgen Tchetgen. 2021. “On Adaptive Estimation of Nonparametric Functionals”. Journal of Machine Learning Research (To Appear)

Rebekka Burkholz, Nilanjana Laha, Rajarshi Mukherjee, and Alkis Gotovos. 2021. “On the Existence of Universal Lottery Tickets.” In International Conference on Learning Representations . Arxiv

Weeberb J. Requia, Stefania Papatheodorou, Petros Koutrakis, Rajarshi Mukherjee, and Henrique L.Roig. 2021. “Increased preterm birth following maternal wildfire smoke exposure in Brazil.” International Journal of Hygiene and Environmental Health, 240. Publisher's Version

Lin Liu, Rajarshi Mukherjee, and James Robins. 2020. “On assumption-free tests and confidence intervals for causal effects estimated by machine learning .” Statistical Science (To Appear). Publisher's Version

Yanjun Han, Jiantao Jiao, and Rajarshi Mukherjee. 2020. “On Estimation of Lr norms in Gaussian White Noise Models.” Probability Theory and Related Fields (To Appear). Publisher's Version

Rajarshi Mukherjee and Subhabrata Sen. 2020. “Testing Degree Correction in Stochastic Block Models.” Annales de l'Institut Henri Poincaré, Probabilités et Statistiques (To Appear). Arxiv

Rajarshi Mukherjee and Gourab Ray. 2020. “On Testing for Parameters in Ising Models.” Annales de l'Institut Henri Poincaré, Probabilités et Statistiques (To Appear). Arxiv

Rajarshi Mukherjee and Bodhisattva Sen. 2019. “On Efficiency of the Plug-in Principle for Estimating Smooth Integrated Functionals of a Nonincreasing Density .” Electronic Journal of Statistics, 13 , Pp. 4416–4448. Publisher's Version

Rajarshi Mukherjee, Sumit Mukherjee, and Subhabrata Sen. 2018. “Detection Thresholds for the ß-Model on Sparse Graphs.” Annals of Statistics, 46, 3, Pp. 1288-1317. Publisher's Version

Rajarshi Mukherjee, Sumit Mukherjee, and Ming Yuan. 2018. “Global testing against sparse alternatives under Ising models.” Annals of Statistics, 46, 5, Pp. 2062-2093. Publisher's Version

Rajarshi Mukherjee and Subhabrata Sen. 2018. “Optimal adaptive inference in random design binary regression.” Bernoulli, 24, 1, Pp. 699-739. Publisher's Version

Kinjal Basu and Rajarshi Mukherjee. 2017. “Asymptotic Normality of Scrambled Geometric Net Quadrature.” Annals of Statistics, 45, 4, Pp. 1759–1788. Publisher's Version

Ian Barnett, Rajarshi Mukherjee, and Xihong Lin. 2017. “The Generalized Higher Criticism for Testing SNP-sets in Genetic Association Testing.” Journal of American Statistical Association, Applications and Case Studies, 112, 517, Pp. 64-76 . Publisher's Version