Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : CRIStAL (Lille, France), NUS (Singapore)
Durée : 3 years
Contact : remi.bardenet@gmail.com
Date limite de publication : 2022-07-31

Contexte :
URL: http://rbardenet.github.io/pdf/phd-proposal.pdf
Context: “Baccarat” AI chair.
Supervisors: Rémi Bardenet (CNRS, Univ. Lille) and Subhro Ghosh (NUS, Singapore).

A point process is a random discrete set of points in a generic space. A broad interest has emerged in ML and signal processing around point processes that exhibit a regular, repulsive arrangement of their points. For instance, sampling repulsive (i.e., diverse) minibatches yields variance reduction in stochastic gradient descent (Bardenet, Ghosh, and Lin, NeurIPS 2021). As another example, moments of pure silence in the musical score of white noise are a repulsive point process that can be leveraged for signal detection (Bardenet, Flamant, and Chainais, ACHA 2020).

Sujet :
To get acquainted with the interdisciplinary topic of repulsive point processes, we shall start with a project that fits in ongoing collaboration between the two supervisors. Ideally, this project shall be tackled during a master’s level internship prior to starting the PhD. Depending on the student’s background and taste, this can be, e.g., (i) topological data analysis applied to the zeros of random spectrograms (the technical for a time-frequency musical score). Alternately, the internship could revolve around (ii) negatively dependent subsampling for large-scale machine learning. For instance, how can we efficiently build repulsive minibatches in stochastic gradient descent?

After this first project, the three of us will pick an ambitious open problem in line with the objectives of the Baccarat AI chair, according to the student’s interest. Candidate problems include identifying and studying repulsive point processes for high-dimensional Monte Carlo integration, fast sampling algorithms for determinantal point processes in machine learning, dictionary learning for signal processing, or studying zeros of wavelet transforms of random signals to use them in filtering tasks.

Profil du candidat :
The ideal candidate has a strong background in either probability, statistics, ML, or signal processing, and a taste for interdisciplinarity.

Formation et compétences requises :
A master in either probability, statistics, ML, or signal processing.

Adresse d’emploi :
Centre de recherche en informatique, signal et automatique de Lille; Department of Statistics and Data Science, National University of Singapore.