39 citations · 107 across the 31 of their papers we have counts for
34 papers
Pooling Versus Ensembling for Ridge Regression Under Covariate Shift
Maya Ramchandran, Rajarshi Mukherjee
Datasets in many settings naturally partition into clusters arising from sub-populations, batch effects, or aggregation across multiple sources. A common response to such heterogen…
Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators
Lin Liu, Rajarshi Mukherjee, James M. Robins
The class of doubly-robust (DR) functionals studied by Rotnitzky et al. (2021) is of central importance in economics and biostatistics. It strictly includes both (i) the class of m…
Nuisance Function Tuning and Sample Splitting for Optimally Estimating a Doubly Robust Functional
Sean McGrath, Rajarshi Mukherjee
Estimators of doubly robust functionals typically rely on estimating two complex nuisance functions, such as the propensity score and conditional outcome mean for the average treat…
Sparse Signal Detection in Heteroscedastic Gaussian Sequence Models: Sharp Minimax Rates
Julien Chhor, Rajarshi Mukherjee, Subhabrata Sen
Given a heterogeneous Gaussian sequence model with unknown mean and known covariance matrix , we study the signal det…
PC Adjusted Testing for Low Dimensional Parameters
Sohom Bhattacharya, Rounak Dey, Rajarshi Mukherjee
In this paper, we investigate the impact of high-dimensional Principal Component (PC) adjustments on inferring the effects of variables on outcomes, with a focus on applications in…
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond
Kuanhao Jiang, Rajarshi Mukherjee, Subhabrata Sen +1
Estimation of the average treatment effect (ATE) is a central problem in causal inference. In recent times, inference for the ATE in the presence of high-dimensional covariates has…
Double sampling for informatively missing data in electronic health record-based comparative effectiveness research
Alexander W. Levis, Rajarshi Mukherjee, Rui Wang +2
Missing data arise in most applied settings and are ubiquitous in electronic health records (EHR). When data are missing not at random (MNAR) with respect to measured covariates, s…
On the Existence of Universal Lottery Tickets
Rebekka Burkholz, Nilanjana Laha, Rajarshi Mukherjee +1
The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation. Rece…
Finding the Optimal Dynamic Treatment Regime Using Smooth Fisher Consistent Surrogate Loss
Nilanjana Laha, Aaron Sonabend-W, Rajarshi Mukherjee +1
Large health care data repositories such as electronic health records (EHR) open new opportunities to derive individualized treatment strategies for complicated diseases such as se…
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…
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…
On Statistical Inference with High Dimensional Sparse CCA
Nilanjana Laha, Nathan Huey, Brent Coull +1
We consider asymptotically exact inference on the leading canonical correlation directions and strengths between two high dimensional vectors under sparsity restrictions. In this r…
Sparse Uniformity Testing
Bhaswar B. Bhattacharya, Rajarshi Mukherjee
In this paper we consider the uniformity testing problem for high-dimensional discrete distributions (multinomials) under sparse alternatives. More precisely, we derive sharp detec…
On Support Recovery with Sparse CCA: Information Theoretic and Computational Limits
Nilanjana Laha, Rajarshi Mukherjee
In this paper we consider asymptotically exact support recovery in the context of high dimensional and sparse Canonical Correlation Analysis (CCA). Our main results describe four r…
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…
Cross-Cluster Weighted Forests
Maya Ramchandran, Rajarshi Mukherjee, Giovanni Parmigiani
Building trustworthy machine learning algorithms for biological applications requires adapting to data heterogeneity from different sources, batches, distributions, or studies. We…
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…
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…
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…
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…
On Testing for Parameters in Ising Models
Rajarshi Mukherjee, Gourab Ray
We consider testing for the parameters of Ferromagnetic Ising models. While testing for the presence of possibly sparse magnetizations, we provide a general lower bound of minimax…
On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
Lin Liu, Rajarshi Mukherjee, James M. Robins
For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators are the state-of-the-art, incorporating the good prediction performance…
On Efficiency of the Plug-in Principle for Estimating Smooth Integrated Functionals of a Nonincreasing Density
Rajarshi Mukherjee, Bodhisattva Sen
We consider the problem of estimating smooth integrated functionals of a monotone nonincreasing density on using the nonparametric maximum likelihood based plug-in…
On Estimation of -Norms in Gaussian White Noise Models
Yanjun Han, Jiantao Jiao, Rajarshi Mukherjee
We provide a complete picture of asymptotically minimax estimation of -norms (for any ) of the mean in Gaussian white noise model over Nikolskii-Besov spaces. In this…