120 citations · 124 across the 17 of their papers we have counts for
17 papers
Chess Signatures of Play
Christian Turk, Nicholas Polson
A game of chess is a stream: a time-ordered sequence of moves, each carrying an engine evaluation, a measure of accuracy, a measure of position complexity, and a clock reading. We…
E-Values, Bayes Risk, Dual Role of Markov's Inequality
Nicholas G. Polson, Daniel Zantedeschi
Two approaches to hypothesis testing, e-value testing and Bayes risk minimisation, both invoke Markov's inequality to control error probabilities. They differ in which distribution…
Kakeya Conjecture and Conditional Kolmogorov Complexity
Nicholas G. Polson, Daniel Zantedeschi
This paper develops an information-theoretic framework for algorithmic complexity under regular identifiable fibering. The central question is: when a decoder is given information…
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…
Bayes with No Shame: Admissibility Geometries of Predictive Inference
Nicholas G. Polson, Daniel Zantedeschi
Modern predictive systems combine predictors, sequential monitors, prediction sets, and online strategies, each with a different certificate of optimality. We study four criterion-…
Bayes Risk for Goodness of Fit Tests
Nicholas G. Polson, Vadim Sokolov, Daniel Zantedeschi
We develop a unified framework for goodness-of-fit (GOF) testing through the lens of Bayes risk. Classical GOF procedures are commonly calibrated either at fixed significance level…
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…
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…
Pochhammer Priors for Sparse Count Models
Yuexi Wang, Nicholas G. Polson
Bayesian hierarchical models are commonly employed for inference in count datasets, as they account for multiple levels of variation by incorporating prior distributions for parame…
Bayesian Regularization: From Tikhonov to Horseshoe
Nicholas G. Polson, Vadim Sokolov
Bayesian regularization is a central tool in modern-day statistical and machine learning methods. Many applications involve high-dimensional sparse signal recovery problems. The go…
Deep Learning in Characteristics-Sorted Factor Models
Guanhao Feng, Jingyu He, Nicholas G. Polson +1
This paper presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for co…
Dynamic Trees for Learning and Design
Matthew A. Taddy, Robert B. Gramacy, Nicholas G. Polson
Dynamic regression trees are an attractive option for automatic regression and classification with complicated response surfaces in on-line application settings. We create a sequen…