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ORCIDMichael G. Rabbat
Michael Rabbat – Mike Rabbat
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- affiliation: Facebook AI Research, Montreal, Canada
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2020 – today
- 2024
[j50]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)- [c102]Mazda Moayeri
, Michael Rabbat, Mark Ibrahim, Diane Bouchacourt:
Embracing Diversity: Interpretable Zero-shot Classification Beyond One Vector Per Class. FAccT 2024: 2302-2321 - [i86]Lucas Lehnert, Sainbayar Sukhbaatar, Paul McVay, Michael Rabbat, Yuandong Tian:
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping. CoRR abs/2402.14083 (2024) - [i85]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 abs/2403.14421 (2024) - [i84]Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael G. Rabbat, Yann LeCun, Mahmoud Assran, Nicolas Ballas:
Revisiting Feature Prediction for Learning Visual Representations from Video. CoRR abs/2404.08471 (2024) - [i83]Mazda Moayeri, Michael Rabbat, Mark Ibrahim, Diane Bouchacourt:
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class. CoRR abs/2404.16717 (2024) - [i82]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 abs/2406.05183 (2024) - 2023
- [j49]Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael G. Rabbat, Ari S. Morcos:
lo-fi: distributed fine-tuning without communication. Trans. Mach. Learn. Res. 2023 (2023) - [c101]Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, Nicolas Ballas:
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture. CVPR 2023: 15619-15629 - [c100]Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Nicolas Ballas:
The hidden uniform cluster prior in self-supervised learning. ICLR 2023 - [c99]John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael G. Rabbat:
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. ICLR 2023 - [c98]Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat:
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. ICML 2023: 11888-11904 - [i81]Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, Nicolas Ballas:
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture. CoRR abs/2301.08243 (2023) - [i80]Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael G. Rabbat, Carole-Jean Wu, Ilya Mironov:
Green Federated Learning. CoRR abs/2303.14604 (2023) - [i79]Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael G. Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski:
DINOv2: Learning Robust Visual Features without Supervision. CoRR abs/2304.07193 (2023) - [i78]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 abs/2306.07179 (2023) - [i77]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 abs/2309.06497 (2023) - 2022
- [j48]Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael G. Rabbat:
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. Trans. Mach. Learn. Res. 2022 (2022) - [c97]John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, Dzmitry Huba:
Federated Learning with Buffered Asynchronous Aggregation. AISTATS 2022: 3581-3607 - [c96]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: 456-473 - [c95]Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael G. Rabbat, Maziar Sanjabi, Lin Xiao:
Federated Learning with Partial Model Personalization. ICML 2022: 17716-17758 - [c94]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 - [c93]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 - [c92]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: 156-167 - [c91]Kamalika Chaudhuri, Chuan Guo, Mike Rabbat:
Privacy-aware compression for federated data analysis. UAI 2022: 296-306 - [i76]Kamalika Chaudhuri, Chuan Guo, Mike Rabbat:
Privacy-Aware Compression for Federated Data Analysis. CoRR abs/2203.08134 (2022) - [i75]Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael G. Rabbat, Maziar Sanjabi, Lin Xiao:
Federated Learning with Partial Model Personalization. CoRR abs/2204.03809 (2022) - [i74]Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael G. Rabbat, Nicolas Ballas:
Masked Siamese Networks for Label-Efficient Learning. CoRR abs/2204.07141 (2022) - [i73]Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael G. Rabbat:
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. CoRR abs/2204.13169 (2022) - [i72]Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael G. Rabbat, Inderjit S. Dhillon:
Positive Unlabeled Contrastive Learning. CoRR abs/2206.01206 (2022) - [i71]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 abs/2206.02633 (2022) - [i70]John Nguyen, Kshitiz Malik, Maziar Sanjabi, Michael G. Rabbat:
Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated Learning. CoRR abs/2206.15387 (2022) - [i69]Mahmoud Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Nicolas Ballas:
The Hidden Uniform Cluster Prior in Self-Supervised Learning. CoRR abs/2210.07277 (2022) - [i68]John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael G. Rabbat:
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. CoRR abs/2210.08090 (2022) - [i67]Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael G. Rabbat, Ari S. Morcos:
lo-fi: distributed fine-tuning without communication. CoRR abs/2210.11948 (2022) - [i66]Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat:
The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. CoRR abs/2211.03942 (2022) - 2021
- [j47]Mahmoud Assran, Michael G. Rabbat:
Asynchronous Gradient Push. IEEE Trans. Autom. Control. 66(1): 168-183 (2021) - [c90]Dominic Richards, Mike Rabbat:
Learning with Gradient Descent and Weakly Convex Losses. AISTATS 2021: 1990-1998 - [c89]Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, Michael G. Rabbat:
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples. ICCV 2021: 8423-8432 - [c88]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 - [i65]Dominic Richards, Mike Rabbat:
Learning with Gradient Descent and Weakly Convex Losses. CoRR abs/2101.04968 (2021) - [i64]Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, Michael G. Rabbat:
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples. CoRR abs/2104.13963 (2021) - [i63]John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael G. Rabbat, Mani Malek, Dzmitry Huba:
Federated Learning with Buffered Asynchronous Aggregation. CoRR abs/2106.06639 (2021) - [i62]Robert M. Gower, Aaron Defazio, Michael G. Rabbat:
Stochastic Polyak Stepsize with a Moving Target. CoRR abs/2106.11851 (2021) - [i61]Jose Javier Gonzalez Ortiz, Jonathan Frankle, Mike Rabbat, Ari S. Morcos, Nicolas Ballas:
Trade-offs of Local SGD at Scale: An Empirical Study. CoRR abs/2110.08133 (2021) - [i60]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 M. Hazelwood:
Sustainable AI: Environmental Implications, Challenges and Opportunities. CoRR abs/2111.00364 (2021) - [i59]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 abs/2111.04877 (2021) - 2020
- [j46]Usman A. Khan, Waheed U. Bajwa, Angelia Nedic, Michael G. Rabbat, Ali H. Sayed:
Optimization for Data-Driven Learning and Control. Proc. IEEE 108(11): 1863-1868 (2020) - [j45]Mahmoud Assran, Arda Aytekin, Hamid Reza Feyzmahdavian, Mikael Johansson, Michael G. Rabbat:
Advances in Asynchronous Parallel and Distributed Optimization. Proc. IEEE 108(11): 2013-2031 (2020) - [c87]Julien M. Hendrickx, Michael G. Rabbat:
Stability of Decentralized Gradient Descent in Open Multi-Agent Systems. CDC 2020: 4885-4890 - [c86]Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Michael G. Rabbat:
Lookahead Converges to Stationary Points of Smooth Non-convex Functions. ICASSP 2020: 8604-8608 - [c85]Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Michael G. Rabbat:
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum. ICLR 2020 - [c84]Mahmoud Assran, Mike Rabbat:
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings. ICML 2020: 410-420 - [i58]Florian Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova, Jure Zbontar, Michael G. Rabbat, Aaron Defazio, Matthew J. Muckley, Daniel K. Sodickson, C. Lawrence Zitnick, Michael P. Recht:
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. CoRR abs/2001.02518 (2020) - [i57]Mahmoud Assran, Michael G. Rabbat:
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings. CoRR abs/2002.12414 (2020) - [i56]Mahmoud Assran, Nicolas Ballas, Lluís Castrejón, Michael G. Rabbat:
Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations. CoRR abs/2006.10803 (2020) - [i55]Mahmoud Assran, Arda Aytekin, Hamid Reza Feyzmahdavian, Mikael Johansson, Michael G. Rabbat:
Advances in Asynchronous Parallel and Distributed Optimization. CoRR abs/2006.13838 (2020) - [i54]Julien M. Hendrickx, Michael G. Rabbat:
Stability of Decentralized Gradient Descent in Open Multi-Agent Systems. CoRR abs/2009.05445 (2020) - [i53]Shagun Sodhani, Olivier Delalleau, Mahmoud Assran, Koustuv Sinha, Nicolas Ballas, Michael G. Rabbat:
A Closer Look at Codistillation for Distributed Training. CoRR abs/2010.02838 (2020) - [i52]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 abs/2011.02999 (2020)
2010 – 2019
- 2019
- [j44]Xiaowen Dong, Dorina Thanou, Michael G. Rabbat, Pascal Frossard:
Learning Graphs From Data: A Signal Representation Perspective. IEEE Signal Process. Mag. 36(3): 44-63 (2019) - [j43]Aida Nowzari, Michael G. Rabbat:
Improved Bounds for Max Consensus in Wireless Networks. IEEE Trans. Signal Inf. Process. over Networks 5(2): 305-319 (2019) - [j42]Jun Ye Yu, Mark J. Coates, Michael G. Rabbat:
Graph-Based Compression for Distributed Particle Filters. IEEE Trans. Signal Inf. Process. over Networks 5(3): 404-417 (2019) - [c83]Nicolas Loizou, Michael G. Rabbat, Peter Richtárik:
Provably Accelerated Randomized Gossip Algorithms. ICASSP 2019: 7505-7509 - [c82]Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat:
Stochastic Gradient Push for Distributed Deep Learning. ICML 2019: 344-353 - [c81]Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau:
TarMAC: Targeted Multi-Agent Communication. ICML 2019: 1538-1546 - [c80]Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat:
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. NeurIPS 2019: 13299-13309 - [i51]Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat:
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. CoRR abs/1906.04585 (2019) - [i50]Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Michael G. Rabbat:
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum. CoRR abs/1910.00643 (2019) - [i49]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 abs/1910.04054 (2019) - 2018
- [j41]Babak Fotouhi, Michael G. Rabbat:
Temporal evolution of the degree distribution of alters in growing networks. Netw. Sci. 6(1): 97-155 (2018) - [j40]Angelia Nedic, Alex Olshevsky, Michael G. Rabbat:
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization. Proc. IEEE 106(5): 953-976 (2018) - [j39]Ahmet Iscen, Teddy Furon, Vincent Gripon, Michael G. Rabbat, Hervé Jégou:
Memory Vectors for Similarity Search in High-Dimensional Spaces. IEEE Trans. Big Data 4(1): 65-77 (2018) - [j38]Bastien Pasdeloup, Vincent Gripon, Grégoire Mercier, Dominique Pastor, Michael G. Rabbat:
Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals. IEEE Trans. Signal Inf. Process. over Networks 4(3): 481-496 (2018) - [c79]Yingxue Zhang, Michael G. Rabbat:
A Graph-CNN for 3D Point Cloud Classification. ICASSP 2018: 6279-6283 - [i48]Mahmoud Assran, Michael G. Rabbat:
Asynchronous Subgradient-Push. CoRR abs/1803.08950 (2018) - [i47]Xiaowen Dong, Dorina Thanou, Michael G. Rabbat, Pascal Frossard:
Learning Graphs from Data: A Signal Representation Perspective. CoRR abs/1806.00848 (2018) - [i46]Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Michael G. Rabbat, Joelle Pineau:
TarMAC: Targeted Multi-Agent Communication. CoRR abs/1810.11187 (2018) - [i45]Nicolas Loizou, Michael G. Rabbat, Peter Richtárik:
Provably Accelerated Randomized Gossip Algorithms. CoRR abs/1810.13084 (2018) - [i44]Jure Zbontar, Florian Knoll, Anuroop Sriram, Matthew J. Muckley, Mary Bruno, Aaron Defazio, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael G. Rabbat, Pascal Vincent, James Pinkerton, Duo Wang, Nafissa Yakubova, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui:
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. CoRR abs/1811.08839 (2018) - [i43]Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat:
Stochastic Gradient Push for Distributed Deep Learning. CoRR abs/1811.10792 (2018) - [i42]Yingxue Zhang, Michael G. Rabbat:
A Graph-CNN for 3D Point Cloud Classification. CoRR abs/1812.01711 (2018) - [i41]Naghmeh Momeni, Michael G. Rabbat:
Effectiveness of Alter Sampling in Social Networks. CoRR abs/1812.03096 (2018) - 2017
- [j37]Pascal Frossard, Pier Luigi Dragotti, Antonio Ortega, Michael G. Rabbat, Alejandro Ribeiro:
Introduction to the IEEE Journal on Selected Topics in Signal Processing and IEEE Transactions on Signal and Information Processing Over Networks Joint Special Issue on Graph Signal Processing. IEEE J. Sel. Top. Signal Process. 11(6): 771-773 (2017) - [j36]Pascal Frossard, Pier Luigi Dragotti, Antonio Ortega, Michael G. Rabbat, Alejandro Ribeiro:
Cooperative Special Issue on Graph Signal Processing in the IEEE Journal of Selected Topics in Signal Processing and the IEEE Transactions on Signal and Information Processing Over Networks. IEEE Trans. Signal Inf. Process. over Networks 3(3): 448-450 (2017) - [j35]Naghmeh Momeni, Michael G. Rabbat:
Inferring Structural Characteristics of Networks With Strong and Weak Ties From Fixed-Choice Surveys. IEEE Trans. Signal Inf. Process. over Networks 3(3): 513-525 (2017) - [j34]Sean F. Lawlor, Michael G. Rabbat:
Time-Varying Mixtures of Markov Chains: An Application to Road Traffic Modeling. IEEE Trans. Signal Process. 65(12): 3152-3167 (2017) - [j33]Augustin-Alexandru Saucan, Mark J. Coates, Michael G. Rabbat:
A Multisensor Multi-Bernoulli Filter. IEEE Trans. Signal Process. 65(20): 5495-5509 (2017) - [c78]Jun Ye Yu, Augustin-Alexandru Saucan, Mark Coates, Michael G. Rabbat:
Algorithms for the multi-sensor assignment problem in the δ-generalized labeled multi-Bernoulli filter. CAMSAP 2017: 1-5 - [c77]Mahmoud Assran, Michael G. Rabbat:
An empirical comparison of multi-agent optimization algorithms. GlobalSIP 2017: 573-577 - [c76]Michael G. Rabbat:
Inferring sparse graphs from smooth signals with theoretical guarantees. ICASSP 2017: 6533-6537 - [i40]Naghmeh Momeni, Michael G. Rabbat:
Inferring Structural Characteristics of Networks with Strong and Weak Ties from Fixed-Choice Surveys. CoRR abs/1706.07828 (2017) - [i39]Angelia Nedic, Alex Olshevsky, Michael G. Rabbat:
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization. CoRR abs/1709.08765 (2017) - 2016
- [j32]Farhad Farokhi, Iman Shames, Michael G. Rabbat, Mikael Johansson:
On reconstructability of quadratic utility functions from the iterations in gradient methods. Autom. 66: 254-261 (2016) - [j31]Jun Ye Yu, Mark J. Coates, Michael G. Rabbat, Stéphane Blouin:
A Distributed Particle Filter for Bearings-Only Tracking on Spherical Surfaces. IEEE Signal Process. Lett. 23(3): 326-330 (2016) - [j30]Santosh Nannuru, Stéphane Blouin, Mark Coates, Michael G. Rabbat:
Multisensor CPHD filter. IEEE Trans. Aerosp. Electron. Syst. 52(4): 1834-1854 (2016) - [j29]Themistoklis Charalambous, Michael G. Rabbat, Mikael Johansson, Christoforos N. Hadjicostis:
Distributed Finite-Time Computation of Digraph Parameters: Left-Eigenvector, Out-Degree and Spectrum. IEEE Trans. Control. Netw. Syst. 3(2): 137-148 (2016) - [j28]Sindri Magnússon, Pradeep Chathuranga Weeraddana, Michael G. Rabbat, Carlo Fischione:
On the Convergence of Alternating Direction Lagrangian Methods for Nonconvex Structured Optimization Problems. IEEE Trans. Control. Netw. Syst. 3(3): 296-309 (2016) - [j27]Xiaoran Jiang, Vincent Gripon, Claude Berrou, Michael G. Rabbat:
Storing Sequences in Binary Tournament-Based Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 27(5): 913-925 (2016) - [j26]Sean F. Lawlor, Timothy Sider, Naveen Eluru, Marianne Hatzopoulou, Michael G. Rabbat:
Detecting Convoys Using License Plate Recognition Data. IEEE Trans. Signal Inf. Process. over Networks 2(3): 391-405 (2016) - [j25]Konstantinos I. Tsianos, Michael G. Rabbat:
Efficient Distributed Online Prediction and Stochastic Optimization With Approximate Distributed Averaging. IEEE Trans. Signal Inf. Process. over Networks 2(4): 489-506 (2016) - [j24]François Leduc-Primeau, Vincent Gripon, Michael G. Rabbat, Warren J. Gross:
Fault-Tolerant Associative Memories Based on c-Partite Graphs. IEEE Trans. Signal Process. 64(4): 829-841 (2016) - [c75]Themistoklis Charalambous, Christoforos N. Hadjicostis, Michael G. Rabbat, Mikael Johansson:
Totally asynchronous distributed estimation of eigenvector centrality in digraphs with application to the PageRank problem. CDC 2016: 25-30 - [c74]Ahmet Iscen, Michael G. Rabbat, Teddy Furon:
Efficient Large-Scale Similarity Search Using Matrix Factorization. CVPR 2016: 2073-2081 - [c73]Michael G. Rabbat, Mark Coates, Stéphane Blouin:
Graph Laplacian distributed particle filtering. EUSIPCO 2016: 1493-1497 - [c72]Jun Ye Yu, Mark Coates, Michael G. Rabbat:
Distributed multi-sensor CPHD filter using pairwise gossiping. ICASSP 2016: 3176-3180 - [c71]Sean F. Lawlor, Michael G. Rabbat:
Estimation of time-varying mixture models: An application to traffic estimation. SSP 2016: 1-5 - [c70]Naghmeh Momeni, Michael G. Rabbat:
Inferring network properties from fixed-choice design with strong and weak ties. SSP 2016: 1-5 - [i38]Naghmeh Momeni, Michael G. Rabbat:
Qualities and Inequalities in Online Social Networks through the Lens of the Generalized Friendship Paradox. CoRR abs/1602.03739 (2016) - [i37]Bastien Pasdeloup, Vincent Gripon, Grégoire Mercier, Dominique Pastor, Michael G. Rabbat:
Characterization and inference of weighted graph topologies from observations of diffused signals. CoRR abs/1605.02569 (2016) - [i36]Bastien Pasdeloup, Michael G. Rabbat, Vincent Gripon, Dominique Pastor, Grégoire Mercier:
Graph reconstruction from the observation of diffused signals. CoRR abs/1605.05251 (2016) - 2015
- [j23]