activity
20182021
most citedOn Minimax Exponents of Sparse Testing

3 citations · 10 across the 7 of their papers we have counts for

collaborators

10 papers

stat.ME20211 cited

Efficient and Robust Semi-supervised Estimation of ATE with Partially Annotated Treatment and Response

Jue Hou, Rajarshi Mukherjee, Tianxi Cai

A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect assessment is the lack of precise information on important clinical variables, including the…

math.ST2021

Sharp Signal Detection Under Ferromagnetic Ising Models

Sohom Bhattacharya, Rajarshi Mukherjee, Gourab Ray

In this paper we study the effect of dependence on detecting a class of structured signals in Ferromagnetic Ising models. Natural examples of our class include Ising Models on latt…

math.ST20213 cited

On Ensembling vs Merging: Least Squares and Random Forests under Covariate Shift

Maya Ramchandran, Rajarshi Mukherjee

It has been postulated and observed in practice that for prediction problems in which covariate data can be naturally partitioned into clusters, ensembling algorithms based on suit…

math.PR20201 cited

Detecting Structured Signals in Ising Models

Nabarun Deb, Rajarshi Mukherjee, Sumit Mukherjee +1

In this paper, we study the effect of dependence on detecting a class of signals in Ising models, where the signals are present in a structured way. Examples include Ising Models o…

cs.LG20202 cited

Semi-Supervised Off Policy Reinforcement Learning

Aaron Sonabend-W, Nilanjana Laha, Ashwin N. Ananthakrishnan +2

Reinforcement learning (RL) has shown great success in estimating sequential treatment strategies which take into account patient heterogeneity. However, health-outcome information…

stat.ME2020

Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

Lin Liu, Rajarshi Mukherjee, James M. Robins

This is the rejoinder to the discussion by Kennedy, Balakrishnan and Wasserman on the paper "On nearly assumption-free tests of nominal confidence interval coverage for causal para…