19 citations · 42 across the 13 of their papers we have counts for
13 papers
BLOC: A Global Optimization Framework for Sparse Covariance Estimation with Non-Convex Penalties
Priyam Das, Trambak Banerjee, Prajamitra Bhuyan
We introduce BLOC (Black-box Optimization over Correlation matrices), a general framework for sparse covariance estimation with non-convex penalties. BLOC operates on the manifold…
Multiple Testing of Partial Conjunction Hypotheses for Assessing Replicability Across Dependent Studies
Monitirtha Dey, Trambak Banerjee, Prajamitra Bhuyan +1
Replicability is central to scientific progress, and the partial conjunction (PC) hypothesis testing framework provides an objective tool to quantify it across disciplines. Existin…
Harnessing The Collective Wisdom: Fusion Learning Using Decision Sequences From Diverse Sources
Trambak Banerjee, Bowen Gang, Jianliang He
We introduce an Integrative Ranking and Thresholding (IRT) framework for fusing evidence from multiple testing procedures. The key innovation is a method that transforms binary tes…
Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach
Jiajun Luo, Trambak Banerjee, Gourab Mukherjee +1
We investigate the problem of compound estimation of normal means while accounting for the presence of side information. Leveraging the empirical Bayes framework, we develop a nonp…
Large-Scale Multiple Testing of Composite Null Hypotheses Under Heteroskedasticity
Bowen Gang, Trambak Banerjee
Heteroskedasticity poses several methodological challenges in designing valid and powerful procedures for simultaneous testing of composite null hypotheses. In particular, the conv…
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity
Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee
Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivari…
Do financial regulators act in the public's interest? A Bayesian latent class estimation framework for assessing regulatory responses to banking crises
Padma Sharma, Trambak Banerjee
When banks fail amidst financial crises, the public criticizes regulators for bailing out or liquidating specific banks, especially the ones that gain attention due to their size o…
Rethinking gradient sparsification as total error minimization
Atal Narayan Sahu, Aritra Dutta, Ahmed M. Abdelmoniem +3
Gradient compression is a widely-established remedy to tackle the communication bottleneck in distributed training of large deep neural networks (DNNs). Under the error-feedback fr…
A Nearest-Neighbor Based Nonparametric Test for Viral Remodeling in Heterogeneous Single-Cell Proteomic Data
Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee
An important problem in contemporary immunology studies based on single-cell protein expression data is to determine whether cellular expressions are remodeled post infection by a…
Nonparametric Empirical Bayes Estimation on Heterogeneous Data
Trambak Banerjee, Luella J. Fu, Gareth M. James +2
The simultaneous estimation of many parameters based on data collected from corresponding studies is a key research problem that has received renewed attention in the high-dimensio…
A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
Trambak Banerjee, Qiang Liu, Gourab Mukherjee +1
We develop a Nonparametric Empirical Bayes (NEB) framework for compound estimation in the discrete linear exponential family, which includes a wide class of discrete distributions…
Adaptive Sparse Estimation with Side Information
Trambak Banerjee, Gourab Mukherjee, Wenguang Sun
The article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general fr…
Feature Screening in Large Scale Cluster Analysis
Trambak Banerjee, Gourab Mukherjee, Peter Radchenko
We propose a novel methodology for feature screening in clustering massive datasets, in which both the number of features and the number of observations can potentially be very lar…