Marc-H Lambert

PhD student at Sierra [https://www.di.ens.fr/sierra/] INRIA-ENS Rue Simone-Iff, Paris marc.lambert [@] inria [DOT] fr Google scholar: [https://scholar.google.com/citations?user=a2HnQ6oAAAAJ&hl=fr]

About me

After a MSc in machine learning (MVA), I started a PhD in September 2020 under the supervision of Francis Bach and Silvère Bonnabel. I work on the interface between machine learning and robotics using variational inference to build new algorithms for online learning, kalman filtering and stochastic control. I also work part-time as a research engineer on smart sensors (infrared camera and radar).

Research

We propose new algorithms to solve nonlinear Bayesian estimation problems based on the variational approximation of an arbitrary distribution with a Gaussian or a mixture of Gaussians.

My research interests include:

• Variational inference

• Kalman filtering

• Dynamic programming

• Bayesian Machine Learning

• Robotics

Publications

• Variational Dynamic Programming for Stochastic Optimal Control. M. Lambert, S. Bonnabel and F. Bach
  2024 [https://arxiv.org/abs/2404.14806]

• Low-rank plus diagonal approximations for Riccati-like matrix differential equations. S. Bonnabel, M. Lambert and F. Bach
  2024 Journal on Matrix Analysis and Applications (SIMAX). [https://inria.hal.science/hal-04621158v1/document]

• Variational Gaussian Approximation of the Kushner Optimal Filter. M. Lambert, S. Bonnabel and F. Bach
  2023 International Conference on Geometric Science of Information, 395-404 [https://arxiv.org/pdf/2310.01859.pdf]

• Variational inference via Wasserstein gradient flows. M. Lambert, S. Chewi, F. Bach, S. Bonnabel and P Rigollet
  2022 Advances in Neural Information Processing Systems [https://arxiv.org/abs/2205.15902]

• The continuous-discrete variational Kalman filter (CD-VKF). M. Lambert, S. Bonnabel and F. Bach
  2022 IEEE 61st Conference on Decision and Control (CDC), 6632-6639 [https://inria.hal.science/hal-03665666v2/document]

• The limited-memory recursive variational Gaussian approximation (L-RVGA). M. Lambert, S. Bonnabel and F. Bach
  2023 Statistics and Computing 33 (3), 70 [https://arxiv.org/pdf/2303.14195.pdf]

• The recursive variational Gaussian approximation (R-VGA). M. Lambert, S. Bonnabel and F. Bach
  2021 Statistics and Computing 32 (1), 10 [https://inria.hal.science/hal-03086627/file/RVGA-HAL-v2.pdf]

Reviewer for:

• Journal of Transactions on Automatic Control (TAC)

• Conference on Decision and Control (CDC)

• Annals of Statistics

Code

The algorithms associated to these articles are available here: [https://github.com/marc-h-lambert]