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
December 12, 2024
COMPUTER VISION
EvalGIM: A Library for Evaluating Generative Image Models
Melissa Hall, Oscar Mañas, Reyhane Askari, Mark Ibrahim, Candace Ross, Pietro Astolfi, Tariq Berrada Ifriqi, Marton Havasi, Yohann Benchetrit, Karen Ullrich, Carolina Braga, Abhishek Charnalia, Maeve Ryan, Mike Rabbat, Michal Drozdzal, Jakob Verbeek, Adriana Romero Soriano
December 12, 2024
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
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