FBIMATRIX (Full Bayesian Inference in Matrix and Tensor Factorization Models)

2017-2020

ANR-16-CE23-0014

FBIMATRIX is an international collaborative research project that is jointly supported by the Agence Nationale de la Recherche (ANR) and the Scientific and Technological Research Council of Turkey (TUBITAK).

Scientific Scope:

Matrix and tensor factorization methods provide a unifying view for a broad spectrum of techniques in machine learning and signal processing, providing both sensible statistical models for datasets as well as efficient computational procedures framed as decomposition algorithms. So far, algebraic or optimization based approaches prevailed for computation of such factorizations. In contrast, the FBIMATRIX project aims to develop the state-of-the-art Markov Chain Monte Carlo (MCMC) methods for Full Bayesian Inference in MATRIX and tensor factorization models. The randomization of Monte Carlo is useful in both Bayesian and non-Bayesian analysis such as model selection, model averaging, privacy preservation or simply better accuracy in computing approximate solutions. MCMC methods are generally perceived as being computationally very demanding and impractical, yet by exploiting parallel and distributed computation, we wish to push the state-of-the-art in terms of scalability, statistical efficiency, computational and communication complexity. In fact, we perceive MCMC as a natural general purpose computational tool of the future for inference and model selection in distributed data, eventually complementing optimization for certain big data problems due to its inherently randomized nature. The project will address Bayesian model selection and model averaging for factorization models, using parallel and distributed computation and current advances in Hybrid Monte Carlo methods that simulate an augmented stochastic dynamics. As such, we aim at developing faster algorithms for hard computational problems such as marginal likelihood estimation and improving convergence rates. We will illustrate the practical utility of the developed parallel and distributed MCMC methods on two challenging applications from two domains: audio source separation and missing link prediction.

Partners:

French Side:

  • Télécom ParisTech (general coordinator: Prof. Gaël Richard, scientific coordinator: Assist. Prof. Umut Simsekli)

Turkish Side:

Publications and Pre-prints:

Journals

International Conferences

International Workshops

Preprints

Recent Events:

  • Kickoff meeting, Istanbul, 30 March, 2017
  • Scientific meeting, Paris, 26-30 June, 2017
  • Scientific meeting, Paris, 10-26 August, 2018
  • Scientific meeting, Istanbul, 09-15 October, 2018