We study the distribution of a fully connected neural network with rando...
Consider a random sample (X_1,…,X_n) from an unknown discrete
distributi...
There is a growing interest on large-width asymptotic properties of Gaus...
There is a growing literature on the study of large-width properties of ...
Completely random measures (CRMs) provide a broad class of priors, argua...
Given a lossy-compressed representation, or sketch, of data with values ...
A flexible method is developed to construct a confidence interval for th...
The estimation of coverage probabilities, and in particular of the missi...
There is a recent literature on large-width properties of Gaussian neura...
A flexible conformal inference method is developed to construct confiden...
In this paper, we develop a novel approach to posterior contractions rat...
Species-sampling problems (SSPs) refer to a vast class of statistical
pr...
"Species-sampling" problems (SSPs) refer to a broad class of statistical...
Posterior contractions rates (PCRs) strengthen the notion of Bayesian
co...
In the 1920's, the English philosopher W.E. Johnson introduced a
charact...
Privacy-protecting data analysis investigates statistical methods under
...
In modern deep learning, there is a recent and growing literature on the...
There is a growing interest in the estimation of the number of unseen
fe...
Given n samples from a population of individuals belonging to different
...
In this paper, we consider fully connected feed-forward deep neural netw...
The count-min sketch (CMS) is a time and memory efficient randomized dat...
The count-min sketch (CMS) is a randomized data structure that provides
...
The interplay between infinite-width neural networks (NNs) and classes o...
This paper presents a new approach to the classical problem of quantifyi...
When neural network's parameters are initialized as i.i.d., neural netwo...
We consider fully connected feed-forward deep neural networks (NNs) wher...
While the cost of sequencing genomes has decreased dramatically in recen...
In recent years we have witnessed an explosion of data collected for
dif...
In this paper we consider the problem of dynamic clustering, where clust...
The problem of maximizing cell type discovery under budget constraints i...
Deep neural networks whose parameters are distributed according to typic...
Feature models are popular in machine learning and they have been recent...
Feature allocation models generalize species sampling models by allowing...
Protection against disclosure is a legal and ethical obligation for agen...
Given n samples from a population of individuals belonging to different
...
Kingman's coalescent is one of the most popular models in population
gen...
This paper provides a generalization of a classical result obtained by W...
Detecting associations between microbial composition and sample
characte...
We characterize the class of exchangeable feature allocations assigning
...
We investigate the class of σ-stable Poisson-Kingman random
probability ...