120 citations · 124 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
Entropy-Regularized Inference: A Predictive Approach
Nicholas G. Polson, Daniel Zantedeschi
Predictive inference requires balancing statistical accuracy against informational complexity, yet the choice of complexity measure is usually imposed rather than derived. We treat…
Bayesian Prediction under Moment Conditioning
Nicholas G. Polson, Daniel Zantedeschi
Moment restrictions specify a class of laws, not a predictive model. We obtain one by conditioning an independent sample from a reference law on its empirical moments, and define p…
De Finetti + Sanov = Bayes: Exchangeable Prediction under Moment Constraints
Nicholas G. Polson, Daniel Zantedeschi
We study exchangeable prediction when empirical-moment constraints define each active finite horizon N. The relevant law is the de Finetti mixture conditioned on E_N = {Phat_N in E…
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…