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GERAD seminar

Distributionally robust Kalman filter


Nov 29, 2018   02:15 PM — 03:00 PM

Soroosh Shafieezadeh-Abadeh École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

We study a distributionally robust mean square error estimation problem over a nonconvex Wasserstein ambiguity set containing only normal distributions. We show that the optimal estimator and the least-favorable distribution form a Nash equilibrium. Despite the non-convex nature of the ambiguity set, we prove that the estimation problem is equivalent to a tractable convex program. We further devise a Frank-Wolfe algorithm for this convex program whose direction-searching subproblem can be solved in a quasi-closed form. Using these ingredients, we introduce a distributionally robust Kalman filter that hedges effectively against model risk.

Bio: Soroosh Shafieezadeh-Abadeh received a B.Sc. and an M.Sc degree in Electrical Engineering from University of Tehran, Tehran, Iran. In September 2014, he joined the Risk Analytics and Optimization chair at EPFL. His research interests revolve around optimization, machine learning, statistics, and high-dimensional data analysis.

Free entrance.
Welcome to everyone!

Erick Delage organizer
Angelos Georghiou organizer


Room 4488
André-Aisenstadt Building
Université de Montréal Campus
2920, chemin de la Tour
Montréal QC H3T 1J4

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