Mike Rabbat

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 24

Top Topics

Neural Network Training

Diffusion Models

Top Venues

Publications

Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning. CoRR (2024)
Ouail Kitouni, Niklas Nolte, Diane Bouchacourt, Adina Williams, Mike Rabbat, Mark Ibrahim
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More. CoRR (2024)
George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson
Benchmarking Neural Network Training Algorithms. CoRR (2023)
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba
Federated Learning with Buffered Asynchronous Aggregation. AISTATS (2022)
Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
Privacy-aware compression for federated data analysis. UAI (2022)
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, Nicolas Ballas
Masked Siamese Networks for Label-Efficient Learning. ECCV (31) (2022)
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
PAPAYA: Practical, Private, and Scalable Federated Learning. MLSys (2022)
Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. RecSys (2022)
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood
Sustainable AI: Environmental Implications, Challenges and Opportunities. MLSys (2022)
Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
Privacy-Aware Compression for Federated Data Analysis. CoRR (2022)
Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. CoRR (2022)
Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat
The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. CoRR (2022)
Jose Javier Gonzalez Ortiz, Jonathan Frankle, Mike Rabbat, Ari S. Morcos, Nicolas Ballas
Trade-offs of Local SGD at Scale: An Empirical Study. CoRR (2021)
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim Hazelwood
Sustainable AI: Environmental Implications, Challenges and Opportunities. CoRR (2021)
Dominic Richards, Mike Rabbat
Learning with Gradient Descent and Weakly Convex Losses. CoRR (2021)
Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu
Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. MLSys (2021)
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
Papaya: Practical, Private, and Scalable Federated Learning. CoRR (2021)
Dominic Richards, Mike Rabbat
Learning with Gradient Descent and Weakly Convex Losses. AISTATS (2021)
Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu
CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. CoRR (2020)
Mahmoud Assran, Mike Rabbat
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings. ICML (2020)
Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. CoRR (2019)
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau
TarMAC: Targeted Multi-Agent Communication. ICML (2019)
Viswanath Sivakumar, Tim Rocktäschel, Alexander H. Miller, Heinrich Küttler, Nantas Nardelli, Mike Rabbat, Joelle Pineau, Sebastian Riedel
MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions. CoRR (2019)
Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. NeurIPS (2019)