Thanks to their dependency structure, non-parametric Hidden Markov Model...
Hidden Markov models (HMMs) are flexible tools for clustering dependent ...
Consider a random sample (X_1,…,X_n) from an unknown discrete
distributi...
"Species-sampling" problems (SSPs) refer to a broad class of statistical...
This article studies the asymptotic properties of Bayesian or frequentis...
We study the frontier between learnable and unlearnable hidden Markov mo...
Given n samples from a population of individuals belonging to different
...
One of the recent approaches to explain good performance of neural netwo...
Protection against disclosure is a legal and ethical obligation for agen...
We investigate the problem of deriving adaptive posterior rates of
contr...
A variety of machine learning tasks---e.g., matrix factorization, topic
...
Sparse exchangeable graphs resolve some pathologies in traditional rando...