2 citations · 2 across the 10 of their papers we have counts for
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Joint parameters estimation in cubic tensor model
Sumit Mukherjee, Arnab Sen, Qiang Wu
We study joint parameter estimation from a single observation in high-dimensional Gibbs measures with cubic tensor interactions, motivated by dense ERGMs, arithmetic-progression mo…
A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression
Sumit Mukherjee, Bodhisattva Sen, Subhabrata Sen
We study empirical Bayes estimation in high-dimensional linear regression. To facilitate computationally efficient estimation of the underlying prior, we adopt a variational empiri…
Joint Estimation in Potts Model
Somabha Mukherjee, Sumit Mukherjee, Sayar Karmakar
In this paper, we study estimation of parameters in a two-parameter Potts model with colors and coupling matrix . We characterize concrete sufficient conditions for existe…
Inference on Gaussian mixture models with dependent labels
Seunghyun Lee, Rajarshi Mukherjee, Sumit Mukherjee
Gaussian mixture models are widely used to model data generated from multiple latent sources. Despite its popularity, most theoretical research assumes that the labels are either i…
CLT in high-dimensional Bayesian linear regression with low SNR
Seunghyun Lee, Nabarun Deb, Sumit Mukherjee
We study central limit theorems for linear statistics in high-dimensional Bayesian linear regression with product priors. Unlike the existing literature where the focus is on poste…
Variational Inference for Latent Variable Models in High Dimensions
Chenyang Zhong, Sumit Mukherjee, Bodhisattva Sen
Variational inference (VI) is a popular method for approximating intractable posterior distributions in Bayesian inference and probabilistic machine learning. In this paper, we int…