activity
20182021
most citedGraph signal denoising using -shrinkage priors

2 citations · 2 across the 4 of their papers we have counts for

collaborators

14 papers

q-fin.PM2021

High-dimensional Portfolio Optimization using Joint Shrinkage

Anik Burman, Sayantan Banerjee

We consider the problem of optimizing a portfolio of financial assets, where the number of assets can be much larger than the number of observations. The optimal portfolio weights…

cs.ET2021

ReCo1: A Fault resilient technique of Correlation Sensitive Stochastic Designs

Shyamali Mitra, Sayantan Banerjee, Mrinal Kanti Naskar

In stochastic circuits, major sources of error are correlation errors, soft errors and random fluctuation errors that affect the accuracy and reliability of the circuit. The soft e…

stat.ME2021

Horseshoe shrinkage methods for Bayesian fusion estimation

Sayantan Banerjee

We consider the problem of estimation and structure learning of high dimensional signals via a normal sequence model, where the underlying parameter vector is piecewise constant, o…

math.PR2021

PageRank Asymptotics on Directed Preferential Attachment Networks

Sayan Banerjee, Mariana Olvera-Cravioto

We characterize the tail behavior of the distribution of the PageRank of a uniformly chosen vertex in a directed preferential attachment graph and show that it decays as a power la…

stat.ME20202 cited

Graph signal denoising using -shrinkage priors

Sayantan Banerjee, Weining Shen

We study the graph signal denoising problem by estimating a piecewise constant signal over an undirected graph. We propose a new Bayesian approach that first converts a general gra…

math.PR2020

Ergodicity and steady state analysis for Interference Queueing Networks

Sayan Banerjee, Abishek Sankararaman

We analyze an interacting queueing network on that was introduced in Sankararaman-Baccelli-Foss (2019) as a model for wireless networks. We show that the marginals o…