prod + news JDS à paraître

Author: jchiquet

_bibliography/in_production.bib

@@ -17,3 +17,24 @@ @article{giorgi2024
 The `R` Package `IBMPopSim` facilitates the simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs). The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications in fields including  actuarial science, biology, ecology or epidemiology. `IBMPopSim` overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms  based on thinning methods, which are compiled using the `Rcpp` package while providing a user-friendly interface.
 }
 }
+
+@article{ambroise2024,
+  bibtex_show = {true},
+  author = {Laplante, Félix and Ambroise, Christophe},
+  publisher = {French Statistical Society},
+  title = {Spectral Bridges: Scalable Spectral Clustering Based on Vector Quantization},
+  journal = {Computo},
+  year        = 2024,
+  url = {https://computo.sfds.asso.fr/published-202412-ambroise-spectral/},
+  doi = {10.57750/1gr8-bk61},
+  issn = {2824-7795},
+  type        = {{Research article}},
+  domain      = {Machine Learning},
+  language    = {R},
+  repository  = {published-202412-ambroise-spectral},
+  langid = {en},
+  abstract = {In this paper, Spectral Bridges, a novel clustering algorithm, is introduced. This algorithm builds upon the traditional k-means and spectral clustering frameworks by subdividing data into small Voronoï regions, which are subsequently merged according to a connectivity measure. Drawing inspiration from Support Vector Machine’s margin concept, a non-parametric clustering approach is proposed, building an affinity margin between each pair of Voronoï regions. This approach delineates intricate, non-convex cluster structures and is robust to hyperparameter choice. The numerical experiments underscore Spectral Bridges as a fast, robust, and versatile tool for clustering tasks spanning diverse domains. Its efficacy extends to large-scale scenarios encompassing both real-world and synthetic datasets. The Spectral Bridge algorithm is implemented both in Python (https://pypi.org/project/spectral-bridges) and R (https://github.com/cambroise/spectral-bridges-Rpackage).
+}
+}
+
+

_news/announcement_jds_202412.md

@@ -0,0 +1,7 @@
+---
+date: 2024-12-16 07:59:00-0400
+inline: true
+---
+
+This year, you have the opportunity to express your interest in publishing an extended version of your submission to the [2025 French Statistics Days in Computo](https://jds2025.sciencesconf.org/), by selecting the theme “Interest in Computo submission” when you submit on [SciencesConf](https://jds2025.sciencesconf.org/submission/submit?lang=fr). If the conference program committee considers that your work falls within the scope of our journal, you will be invited to propose a longer version of your submission to Computo. We (the editorial board and technical team) will be able to assist you in bringing your article into the journal's reproducible format, targeting the end of June 2025. The review process will then involve experts in the field, following the recommendations of the conference program committee, with a view to publication in Computo by the end of 2025.
+