updating publication list

Author: jchiquet

_bibliography/in_production.bib

@@ -1,18 +1,3 @@
-@article{elmasri-optimal,
-  bibtex_show = {true},
-  author      = {El Masri, Maxime and Morio, Jérôme and Simatos, Florian},
-  title       = {{Optimal projection for parametric importance sampling in high dimensions}},
-  journal     = {Computo},
-  year        = 2024,
-  abstract    = {In this paper we propose a dimension-reduction strategy in order to improve the performance of importance sampling in high dimension. The idea is to estimate variance terms in a small number of suitably chosen directions. We first prove that the optimal directions, i.e., the ones that minimize the Kullback--Leibler divergence with the optimal auxiliary density, are the eigenvectors associated to extreme (small or large) eigenvalues of the optimal covariance matrix. We then perform extensive numerical experiments that show that as dimension increases, these directions give estimations which are very close to optimal. Moreover, we show that the estimation remains accurate even when a simple empirical estimator of the covariance matrix is used to estimate these directions. These theoretical and numerical results open the way for different generalizations, in particular the incorporation of such ideas in adaptive importance sampling schemes},
-  doi         = {doi.org/10.57750/jjza-6j82},
-  repository  = {published-202402-elmasri-optimal},
-  type        = {{Research article}},
-  language    = {Python},
-  domain      = {Statistics},
-  keywords    = {Rare event simulation, Parameter estimation, Importance sampling, Dimension reduction, Kullback--Leibler divergence, Projection},
-  issn        = {2824-7795}
-}
 
 @article{lefort_peerannot,
   bibtex_show = {true},

_bibliography/published.bib

@@ -1,3 +1,19 @@
+@article{elmasri-optimal,
+  bibtex_show = {true},
+  author      = {El Masri, Maxime and Morio, Jérôme and Simatos, Florian},
+  title       = {{Optimal projection for parametric importance sampling in high dimensions}},
+  journal     = {Computo},
+  year        = 2024,
+  abstract    = {In this paper we propose a dimension-reduction strategy in order to improve the performance of importance sampling in high dimension. The idea is to estimate variance terms in a small number of suitably chosen directions. We first prove that the optimal directions, i.e., the ones that minimize the Kullback--Leibler divergence with the optimal auxiliary density, are the eigenvectors associated to extreme (small or large) eigenvalues of the optimal covariance matrix. We then perform extensive numerical experiments that show that as dimension increases, these directions give estimations which are very close to optimal. Moreover, we show that the estimation remains accurate even when a simple empirical estimator of the covariance matrix is used to estimate these directions. These theoretical and numerical results open the way for different generalizations, in particular the incorporation of such ideas in adaptive importance sampling schemes},
+  doi         = {doi.org/10.57750/jjza-6j82},
+  repository  = {published-202402-elmasri-optimal},
+  type        = {{Research article}},
+  language    = {Python},
+  domain      = {Statistics},
+  keywords    = {Rare event simulation, Parameter estimation, Importance sampling, Dimension reduction, Kullback--Leibler divergence, Projection},
+  issn        = {2824-7795}
+}
+
 @article{adrat_repulsion,
   bibtex_show = {true},
   author      = {Adrat, Hamza and Decreusefond, Laurent},

_pages/publications.md

@@ -30,10 +30,6 @@ Manuscript conditionally accepted, whose editorial and scientific reproducibilit
 
 </div>
 
-## Under review
-
-At the moment, 6 manuscripts are under review.
-
 ## Example: a mock contribution
 
 This  page is  a reworking  of the  original t-SNE  article using  the