Michael Rabbat

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 13

Top Topics

Semantically Heterogeneous

Distributed Data

Learning Process

Top Venues

Trans. Mach. Learn. Res.

Publications

Mazda Moayeri, Michael Rabbat, Mark Ibrahim, Diane Bouchacourt
Embracing Diversity: Interpretable Zero-shot Classification Beyond One Vector Per Class. FAccT (2024)
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski
DINOv2: Learning Robust Visual Features without Supervision. Trans. Mach. Learn. Res. 2024 (2024)
Mazda Moayeri, Michael Rabbat, Mark Ibrahim, Diane Bouchacourt
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class. CoRR (2024)
Lucas Lehnert, Sainbayar Sukhbaatar, Paul McVay, Michael Rabbat, Yuandong Tian
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping. CoRR (2024)
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael Rabbat, Carole-Jean Wu, Ilya Mironov
Green Federated Learning. CoRR (2023)
Hao-Jun Michael Shi, Tsung-Hsien Lee, Shintaro Iwasaki, Jose Gallego-Posada, Zhijing Li, Kaushik Rangadurai, Dheevatsa Mudigere, Michael Rabbat
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale. CoRR (2023)
Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael Rabbat, Maziar Sanjabi, Lin Xiao
Federated Learning with Partial Model Personalization. CoRR (2022)
John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael Rabbat
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. CoRR (2022)
John Nguyen, Kshitiz Malik, Maziar Sanjabi, Michael Rabbat
Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated Learning. CoRR (2022)
Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael Rabbat, Maziar Sanjabi, Lin Xiao
Federated Learning with Partial Model Personalization. ICML (2022)
Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael Rabbat
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. CoRR (2022)
Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael Rabbat, Inderjit S. Dhillon
Positive Unlabeled Contrastive Learning. CoRR (2022)
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek Esmaeili, Dzmitry Huba
Federated Learning with Buffered Asynchronous Aggregation. CoRR (2021)