Michael Rabbat

RESEARCH SCIENTIST | MONTREAL, CANADA

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

April 04, 2024

CORE MACHINE LEARNING

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo

April 04, 2024

February 15, 2024

CORE MACHINE LEARNING

Revisiting Feature Prediction for Learning Visual Representations from Video

Adrien Bardes, Quentin Garrido, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, Nicolas Ballas, Jean Ponce

February 15, 2024

June 18, 2023

CORE MACHINE LEARNING

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Mido Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Mike Rabbat, Yann LeCun, Nicolas Ballas

June 18, 2023

April 26, 2023

CORE MACHINE LEARNING

SYSTEMS RESEARCH

Green Federated Learning

Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk Krüger, Mike Rabbat, Carole-Jean Wu, Ilya Mironov

April 26, 2023

July 13, 2022

Federated Learning with Partial Model Personalization

Lin Xiao, Abdelrahman Mohamed, Kshitiz Malik, Maziar Sanjabi, Mike Rabbat, Krishna Pilllutla

July 13, 2022

April 26, 2022

PAPAYA: PRACTICAL, PRIVATE, AND SCALABLE FEDERATED LEARNING

Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek

April 26, 2022

September 14, 2021

Federated Learning with Buffered Asynchronous Aggregation

John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba

September 14, 2021

December 14, 2020

Stability of Decentralized Gradient Descent in Open Multi-Agent Systems

Mike Rabbat, Julien Hendrickx

December 14, 2020

September 16, 2020

Advances in Asynchronous Parallel and Distributed Optimization

Mike Rabbat, Mido Assran, Arda Aytekin, Hamid Feyzmahdavian, Mikael Johansson

September 16, 2020

February 25, 2020

RESEARCH

Lookahead converges to stationary points of smooth non-convex functions

Mike Rabbat, Jianyu Wang, Nicolas Ballas, Vinayak Tantia

February 25, 2020

February 25, 2020

RESEARCH

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

Mike Rabbat, Nicolas Ballas, Vinayak Tantia, Jianyu Wang

February 25, 2020

February 11, 2019

RESEARCH

Provably Accelerated Randomized Gossip Algorithms

Mike Rabbat, Nicolas Loizou, Peter Richtarik

February 11, 2019