9 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…
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…
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…
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…
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…