9 papers
Bayesian nonparametric inference for modal missing species and features
Alessandro Colombi, Mario Beraha, Daniele Durante +1
Species and feature sampling problems arise naturally whenever each observed unit is associated with one or more labels from a countable alphabet, and inference focuses on the unob…
Confidence intervals for maximum unseen probabilities, with application to sequential sampling design
Alessandro Colombi, Mario Beraha, Amichai Painsky +1
Discovery problems often require deciding whether additional sampling is needed to detect all categories whose prevalence exceeds a prespecified threshold. We study this question u…
Conformal Inference for Open-Set and Imbalanced Classification
Tianmin Xie, Yanfei Zhou, Ziyi Liang +2
This paper presents a conformal prediction method for classification in highly imbalanced and open-set settings, where there are many possible classes and not all may be represente…
Large-scale entity resolution via microclustering Ewens--Pitman random partitions
Mario Beraha, Stefano Favaro
We introduce the microclustering Ewens--Pitman model for random partitions, obtained by scaling the strength parameter of the Ewens--Pitman model linearly with the sample size. The…
Online activity prediction via generalized Indian buffet process models
Mario Beraha, Lorenzo Masoero, Stefano Favaro +1
Online A/B tests are the standard tool for data-driven decision-making at scale. Among the design choices with the largest impact on statistical power is the triggering mechanism:…
Improved prediction of future user activity in online A/B testing
Lorenzo Masoero, Mario Beraha, Thomas Richardson +1
In online randomized experiments or A/B tests, accurate predictions of participant inclusion rates are of paramount importance. These predictions not only guide experimenters in op…
A Nonparametric Bayes Approach to Online Activity Prediction
Mario Beraha, Lorenzo Masoero, Stefano Favaro +1
Accurately predicting the onset of specific activities within defined timeframes holds significant importance in several applied contexts. In particular, accurate prediction of the…
MCMC for Bayesian nonparametric mixture modeling under differential privacy
Mario Beraha, Stefano Favaro, Vinayak Rao
Estimating the probability density of a population while preserving the privacy of individuals in that population is an important and challenging problem that has received consider…
A smoothed-Bayesian approach to frequency recovery from sketched data
Mario Beraha, Stefano Favaro, Matteo Sesia
We provide a novel statistical perspective on a classical problem at the intersection of computer science and information theory: recovering the empirical frequency of a symbol in…