57 citations · 181 across the 23 of their papers we have counts for
24 papers
Variational Inference in high-dimensional linear regression
Sumit Mukherjee, Subhabrata Sen
We study high-dimensional Bayesian linear regression with product priors. Using the nascent theory of non-linear large deviations (Chatterjee and Dembo,2016), we derive sufficient…
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity
Chen Lu, Subhabrata Sen
We study community detection in the contextual stochastic block model arXiv:1807.09596 [cs.SI], arXiv:1607.02675 [stat.ME]. In arXiv:1807.09596 [cs.SI], the second author studied t…
A large deviation principle for block models
Christian Borgs, Jennifer Chayes, Julia Gaudio +2
We initiate a study of large deviations for block model random graphs in the dense regime. Following Chatterjee-Varadhan(2011), we establish an LDP for dense block models, viewed a…
On Minimax Exponents of Sparse Testing
Rajarshi Mukherjee, Subhabrata Sen
We consider exact asymptotics of the minimax risk for global testing against sparse alternatives in the context of high dimensional linear regression. Our results characterize the…
The Overlap Gap Property in Principal Submatrix Recovery
David Gamarnik, Aukosh Jagannath, Subhabrata Sen
We study support recovery for a principal submatrix with elevated mean , hidden in an symmetric mean zero Gaussian matrix. Here is a universal c…
A correction to Kallenberg's theorem for jointly exchangeable random measures
Christian Borgs, Jennifer T. Chayes, Souvik Dhara +1
Kallenberg (2005) provided a necessary and sufficient condition for the local finiteness of a jointly exchangeable random measure on . Here we note an additional condition…