Michael Rabbat

Mike is a founding member of the Facebook AI Research (FAIR) team in Montreal. He holds a B.Eng. from the University of Illinois at Urbana-Champaign, a M.Eng from Rice University, and a Ph.D. in electrical engineering from the University of Wisconsin-Madison. Prior to Facebook, Mike was a professor at McGill University in the Department of Electrical and Computer Engineering. His research interests include optimization, distributed algorithms and signal processing.

Michael's Publications



Provably Accelerated Randomized Gossip Algorithms

In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method – a popular…

Nicolas Loizou, Michael Rabbat, Peter Richtárik,



Learning graphs from data: A signal representation perspective

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the datasets, it…

Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal Frossard,



Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

Multi-simulator training has contributed to the recent success of Deep Reinforcement Learning by stabilizing learning and allowing for higher training throughputs. We propose Gossip-based Actor-Learner Architectures (GALA) where several…

Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Michael Rabbat,