Mike Rabbat - RankMe

30 papers A* 3A 4Journal 19Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures. CoRR Basile Terver, Randall Balestriero, Megi Dervishi, David Fan, Quentin Garrido, Tushar Nagarajan, Koustuv Sinha, Wancong Zhang, Mike Rabbat, Yann LeCun, Amir Bar
2026	J	`jnl`	Interpreting Physics in Video World Models. CoRR Sonia Joseph, Quentin Garrido, Randall Balestriero, Matthew Kowal, Thomas Fel, Shahab Bakhtiari, Blake A. Richards, Mike Rabbat
2026	J	`jnl`	V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning. CoRR Lorenzo Mur-Labadia, Matthew J. Muckley, Amir Bar, Mido Assran, Koustuv Sinha, Mike Rabbat, Yann LeCun, Nicolas Ballas, Adrien Bardes
2025	J	`jnl`	Gaussian Embeddings: How JEPAs Secretly Learn Your Data Density. CoRR Randall Balestriero, Nicolas Ballas, Mike Rabbat, Yann LeCun
2024	J	`jnl`	DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning. CoRR Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo
2024	J	`jnl`	EvalGIM: A Library for Evaluating Generative Image Models. CoRR Melissa Hall, Oscar Mañas, Reyhane Askari Hemmat, 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
2024	J	`jnl`	The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More. CoRR Ouail Kitouni, Niklas Nolte, Diane Bouchacourt, Adina Williams, Mike Rabbat, Mark Ibrahim
2024	J	`jnl`	Transformers Can Navigate Mazes With Multi-Step Prediction. CoRR Niklas Nolte, Ouail Kitouni, Adina Williams, Mike Rabbat, Mark Ibrahim
2023	J	`jnl`	Benchmarking Neural Network Training Algorithms. CoRR 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
2022	A	`conf`	Federated Learning with Buffered Asynchronous Aggregation. AISTATS John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba
2022	—	`conf`	Masked Siamese Networks for Label-Efficient Learning. ECCV (31) Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, Nicolas Ballas
2022	—	`conf`	PAPAYA: Practical, Private, and Scalable Federated Learning. MLSys 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
2022	J	`jnl`	Privacy-Aware Compression for Federated Data Analysis. CoRR Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
2022	A	`conf`	Privacy-aware compression for federated data analysis. UAI Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
2022	—	`conf`	Sustainable AI: Environmental Implications, Challenges and Opportunities. MLSys 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 C. Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood
2022	J	`jnl`	The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. CoRR Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat
2022	A	`conf`	Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. RecSys Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu
2022	J	`jnl`	Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. CoRR Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu
2021	A	`conf`	Learning with Gradient Descent and Weakly Convex Losses. AISTATS Dominic Richards, Mike Rabbat
2021	J	`jnl`	Learning with Gradient Descent and Weakly Convex Losses. CoRR Dominic Richards, Mike Rabbat
2021	J	`jnl`	Papaya: Practical, Private, and Scalable Federated Learning. CoRR 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
2021	J	`jnl`	Sustainable AI: Environmental Implications, Challenges and Opportunities. CoRR 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 C. Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood
2021	J	`jnl`	Trade-offs of Local SGD at Scale: An Empirical Study. CoRR Jose Javier Gonzalez Ortiz, Jonathan Frankle, Mike Rabbat, Ari S. Morcos, Nicolas Ballas
2021	—	`conf`	Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. MLSys 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
2020	J	`jnl`	CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. CoRR 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
2020	A*	`conf`	On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings. ICML Mahmoud Assran, Mike Rabbat
2019	A*	`conf`	Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. NeurIPS Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat
2019	J	`jnl`	Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. CoRR Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat
2019	J	`jnl`	MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions. CoRR Viswanath Sivakumar, Tim Rocktäschel, Alexander H. Miller, Heinrich Küttler, Nantas Nardelli, Mike Rabbat, Joelle Pineau, Sebastian Riedel
2019	A*	`conf`	TarMAC: Targeted Multi-Agent Communication. ICML Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau