There is a growing interest in the estimation of the number of unseen features, mostly driven
by biological applications. A recent work brought out a peculiar property of the popular …

We propose a nonparametric factorization approach for sparsely observed tensors. The
sparsity does not mean zero-valued entries are massive or dominated. Rather, it implies the …

Completely random measures (CRMs) provide a broad class of priors, arguably, the most
popular, for Bayesian nonparametric (BNP) analysis of trait allocations. As a peculiar …

Feature allocation models are an extension of Bayesian nonparametric clustering models,
where individuals can share multiple features. We study a broad class of models whose …

Bayesian nonparametric hierarchical priors provide flexible models for sharing of
information within and across groups. We focus on latent feature allocation models, where …

A unified construction for series representations and finite approximations of completely random
measures Page 1 Bernoulli 29(3), 2023, 2142–2166 https://doi.org/10.3150/22-BEJ1536 A …

Completely random measures provide a principled approach to creating flexible
unsupervised models, where the number of latent features is infinite and the number of …

Overlapping Communities on Large-Scale Networks: Benchmark Generation and Learning via
Adaptive Stochastic Optimization Alessand Page 1 Overlapping Communities on Large-Scale …