The University of Queensland

Stochastic majorization--minimization algorithms for data science (2023–2026)

Abstract:

The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inferential quantities in an incremental manner. The proposed stochastic algorithms encompass and extend upon a wide variety of current algorithmic frameworks for fitting statistical and machine learning models, and can be used to produce feasible and practical algorithms for complex models, both current and future.

Grant type:

ARC Discovery Projects

Researchers:

• 

  Dr Xin Guo

  Snr Lecturer in Mathem Data Science
  

  School of Mathematics and Physics

  Faculty of Science

Funded by:

Australian Research Council