Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Quasi-Bayesian Perspective to Online Clustering   When faced with high frequency streams of data, clustering raises theoretical
and algorithmic pitfalls. We introduce a new and adaptive online clustering
algorithm relying on a quasi-Bayesian approach, with a dynamic (i.e.,
time-dependent) estimation of the (unknown and changing) number of clusters. We
prove that our approach is supported by minimax regret bounds. We also provide
an RJMCMC-flavored implementation (called PACBO, see
https://cran.r-project.org/web/packages/PACBO/index.html) for which we give a
convergence guarantee. Finally, numerical experiments illustrate the potential
of our procedure.

24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
1
The categories of this paper are: cs
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an