---
res:
  bibo_abstract:
  - 'We propose a novel approach to concentration for non-independent random variables.
    The main idea is to ``pretend'''' that the random variables are independent and
    pay a multiplicative price measuring how far they are from actually being independent.
    This price is encapsulated in the Hellinger integral between the joint and the
    product of the marginals, which is then upper bounded leveraging tensorisation
    properties. Our bounds represent a natural generalisation of concentration inequalities
    in the presence of dependence: we recover exactly the classical bounds (McDiarmid''s
    inequality) when the random variables are independent. Furthermore, in a ``large
    deviations'''' regime, we obtain the same decay in the probability as for the
    independent case, even when the random variables display non-trivial dependencies.
    To show this, we consider a number of applications of interest. First, we provide
    a bound for Markov chains with finite state space. Then, we consider the Simple
    Symmetric Random Walk, which is a non-contracting Markov chain, and a non-Markovian
    setting in which the stochastic process depends on its entire past. To conclude,
    we propose an application to Markov Chain Monte Carlo methods, where our approach
    leads to an improved lower bound on the minimum burn-in period required to reach
    a certain accuracy. In all of these settings, we provide a regime of parameters
    in which our bound fares better than what the state of the art can provide.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Amedeo Roberto
      foaf_name: Esposito, Amedeo Roberto
      foaf_surname: Esposito
      foaf_workInfoHomepage: http://www.librecat.org/personId=9583e921-e1ad-11ec-9862-cef099626dc9
  - foaf_Person:
      foaf_givenName: Marco
      foaf_name: Mondelli, Marco
      foaf_surname: Mondelli
      foaf_workInfoHomepage: http://www.librecat.org/personId=27EB676C-8706-11E9-9510-7717E6697425
    orcid: 0000-0002-3242-7020
  bibo_doi: 10.1109/isit54713.2023.10206899
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2157-8117
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: Concentration without independence via information measures@
...