I am a Research Scientist in the Facebook AI Research group. I am currently on leave from McGill University where I am an Associate Professor of Electrical and Computer Engineering. I received a Masters from Rice University in 2003 and a PhD from the University of Wisconsin in 2006, both under the supervision of Robert Nowak.
Interests
Optimization, machine learning, parallel algorithms, graphs and networks, signal processing.
Related Links
Latest Publications
Lookahead converges to stationary points of smooth non-convex functions
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Mike Rabbat
ICASSP - May 4, 2020
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Mike Rabbat
ICLR - April 26, 2020
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat
NeurIPS - December 9, 2019
MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
Viswanath Sivakumar, Tim Rocktäschel, Alexander H. Miller, Heinrich Küttler, Nantas Nardelli, Mike Rabbat, Joelle Pineau, Sebastian Riedel
Workshop on ML for Systems at NeurIPS - November 30, 2019
Stochastic Gradient Push for Distributed Deep Learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbat
ICML - June 10, 2019
Provably Accelerated Randomized Gossip Algorithms
Nicolas Loizou, Mike Rabbat, Peter Richtárik
IEEE ICASSP - May 12, 2019
Learning graphs from data: A signal representation perspective
Xiaowen Dong, Dorina Thanou, Mike Rabbat, Pascal Frossard
IEEE Signal Processing Magazine - May 1, 2019
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization
Angelica Nedic, Alex Olshevsky, Mike Rabbat
Proceedings of the IEEE - May 15, 2018