selected publications

• academic article

  • A Probabilistic Incremental Proximal Gradient Method.  IEEE Signal Processing Letters.  26:1257-1261. 2019
  • Generalized Multiple Importance Sampling.  Statistical Science.  34. 2019
  • Multiple Importance Sampling for Efficient Symbol Error Rate Estimation.  IEEE Signal Processing Letters.  26:420-424. 2019
  • Efficient linear fusion of partial estimators.  HAL (Le Centre pour la Communication Scientifique Directe).  2018
  • The Recycling Gibbs sampler for efficient learning.  Digital Signal Processing.  74:1-13. 2017

• blog posting

  • Accelerating MCMC Algorithms.  arXiv (Cornell University). 2018

• conference paper

  • End-to-End Learning of Gaussian Mixture Proposals Using Differentiable Particle Filters and Neural Networks.  ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 9701-9705. 2024
  • Adaptive Gaussian Nested Filter for Parameter Estimation and State Tracking in Dynamical Systems.  ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1-5. 2023
  • Adaptive importance sampling with scaled Langevin proposal adaptatioń.  . 2019
  • The Incremental Proximal Method: A Probabilistic Perspective.  . 4279-4283. 2018
  • Novel weighting and resampling schemes in Population Monte Carlo.  HAL (Le Centre pour la Communication Scientifique Directe). 2017
  • Group Metropolis Sampling.  HAL (Le Centre pour la Communication Scientifique Directe). 2017
  • Recycling Gibbs Sampling.  HAL (Le Centre pour la Communication Scientifique Directe). 2017
  • A new strategy for effective learning in population Monte Carlo sampling.  2014 48th Asilomar Conference on Signals, Systems and Computers. 1540-1544. 2016

• proceedings

  • Recycling Gibbs Sampling.  Zenodo (CERN European Organization for Nuclear Research). 2018