11 citations · 37 across the 23 of their papers we have counts for
27 papers
Composition as Direction: An Active-Set Ray-Based Model for Sparse High-Dimensional Compositional Data
Michael R Schwob, Jyotishka Datta
[Working Draft] Compositional data are central to microbial, ecological, and environmental research, yet often have four features that are difficult to accommodate jointly: exact z…
A New Look at Bayesian Testing
Jyotishka Datta, Nicholas G. Polson, Vadim Sokolov +1
We identify the critical deviation scale governing Bayesian evidence accumulation in regular parametric testing. Under integrated Bayes risk with zero-one loss, the risk-optimal re…
Conformal Prediction = Bayes?
Jyotishka Datta, Nicholas G. Polson, Vadim Sokolov +1
Conformal prediction (CP) is widely presented as distribution-free predictive inference with finite-sample marginal coverage under exchangeability. We argue that CP is best underst…
Bayesian Global-Local Regularization
Jyotishka Datta, Nick Polson, Vadim Sokolov
We propose a unified framework for global-local regularization that bridges the gap between classical techniques -- such as ridge regression and the nonnegative garotte -- and mode…
Polynomial Log-Marginals and Tweedie's Formula : When Is Bayes Possible?
Jyotishka Datta, Nicholas G. Polson
Motivated by Tweedie's formula for the Compound Decision problem, we examine the theoretical foundations of empirical Bayes estimators that directly model the marginal density $m(y…
Prediction-Powered Inference with Inverse Probability Weighting
Jyotishka Datta, Nicholas G. Polson
Prediction-powered inference (PPI) is a recent framework for valid statistical inference with partially labeled data, combining model-based predictions on a large unlabeled set wit…
Bayesian ICA with super-Gaussian Source Priors
Jyotishka Datta, Soham Ghosh, Nicholas G. Polson
Independent Component Analysis (ICA) plays a central role in modern machine learning as a flexible framework for feature extraction. We introduce a horseshoe-type prior with a late…
Nonparametric Bayes multiresolution testing for high-dimensional rare events
Jyotishka Datta, Sayantan Banerjee, David B. Dunson
In a variety of application areas, there is interest in assessing evidence of differences in the intensity of event realizations between groups. For example, in cancer genomic stud…
Quantile Importance Sampling
Jyotishka Datta, Nicholas G. Polson
In Bayesian inference, the approximation of integrals of the form is a fundamental challenge. Such integrals are cr…
Maximum a Posteriori Estimation in Graphical Models Using Local Linear Approximation
Ksheera Sagar, Jyotishka Datta, Sayantan Banerjee +1
Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in multivariate statistical signal processing; since the sparsity pattern naturally…
Quantifying the Effect of Socio-Economic Predictors and Built Environment on Mental Health Events in Little Rock, AR
Alfieri Ek, Samantha Robinson, Grant Drawve +1
Proper allocation of law enforcement resources remains a critical issue in crime prediction and prevention that operates by characterizing spatially aggregated crime activities and…
Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix
Anindya Bhadra, Ksheera Sagar, David Rowe +2
Marginal likelihood, also known as model evidence, is a fundamental quantity in Bayesian statistics. It is used for model selection using Bayes factors or for empirical Bayes tunin…
Inverse Probability Weighting: from Survey Sampling to Evidence Estimation
Jyotishka Datta, Nicholas Polson
We consider the class of inverse probability weight (IPW) estimators, including the popular Horvitz-Thompson and Hajek estimators used routinely in survey sampling, causal inferenc…
Merging Two Cultures: Deep and Statistical Learning
Anindya Bhadra, Jyotishka Datta, Nick Polson +2
Merging the two cultures of deep and statistical learning provides insights into structured high-dimensional data. Traditional statistical modeling is still a dominant strategy for…
Precision Matrix Estimation under the Horseshoe-like Prior-Penalty Dual
Ksheera Sagar, Sayantan Banerjee, Jyotishka Datta +1
Precision matrix estimation in a multivariate Gaussian model is fundamental to network estimation. Although there exist both Bayesian and frequentist approaches to this, it is diff…
On Posterior consistency of Bayesian Changepoint models
Nilabja Guha, Jyotishka Datta
While there have been a lot of recent developments in the context of Bayesian model selection and variable selection for high dimensional linear models, there is not much work in t…
Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Block-Correlated Predictors
Jonathan Boss, Jyotishka Datta, Xin Wang +3
Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for sparse estimation problems. However, there is limited work extending these priors to pred…
FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints
Souradip Chakraborty, Ekansh Verma, Saswata Sahoo +1
Representation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold. Moreover, f…
Horseshoe Regularization for Machine Learning in Complex and Deep Models
Anindya Bhadra, Jyotishka Datta, Yunfan Li +1
Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machin…
Joint Mean-Covariance Estimation via the Horseshoe with an Application in Genomic Data Analysis
Yunfan Li, Jyotishka Datta, Bruce A. Craig +1
Seemingly unrelated regression is a natural framework for regressing multiple correlated responses on multiple predictors. The model is very flexible, with multiple linear regressi…
Lasso Meets Horseshoe : A Survey
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. La…
Horseshoe Regularization for Feature Subset Selection
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics. The penalty is ideal for this task, the cavea…
Prediction risk for the horseshoe regression
Anindya Bhadra, Jyotishka Datta, Yunfan Li +2
We show that prediction performance for global-local shrinkage regression can overcome two major difficulties of global shrinkage regression: (i) the amount of relative shrinkage i…
Global-Local Mixtures
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
Global-local mixtures are derived from the Cauchy-Schlomilch and Liouville integral transformation identities. We characterize well-known normal-scale mixture distributions includi…