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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]Syamantak Datta Gupta, Mark Coates, Michael G. Rabbat:
Error Propagation in Gossip-Based Distributed Particle Filters. IEEE Trans. Signal Inf. Process. over Networks 1(3): 148-163 (2015) - [c69]Bastien Pasdeloup, Michael G. Rabbat, Vincent Gripon, Dominique Pastor, Grégoire Mercier:
Graph reconstruction from the observation of diffused signals. Allerton 2015: 1386-1390 - [c68]Chon-Wang Chao, Michael G. Rabbat, Stéphane Blouin:
Particle weight approximation with clustering for gossip-based distributed particle filters. CAMSAP 2015: 85-88 - [c67]Michael G. Rabbat:
Multi-agent mirror descent for decentralized stochastic optimization. CAMSAP 2015: 517-520 - [c66]Jun Ye Yu, Michael G. Rabbat, Mark Coates, Stéphane Blouin:
Performance investigation on constraint sufficient statistics distributed particle filter. CCECE 2015: 1526-1531 - [c65]Naghmeh Momeni, Michael G. Rabbat:
Measuring the Generalized Friendship Paradox in Networks with Quality-Dependent Connectivity. CompleNet 2015: 45-55 - [c64]Bastien Pasdeloup, Réda Alami, Vincent Gripon, Michael G. Rabbat:
Toward an uncertainty principle for weighted graphs. EUSIPCO 2015: 1496-1500 - [c63]Santosh Nannuru, Mark Coates, Michael G. Rabbat, Stéphane Blouin:
General solution and approximate implementation of the multisensor multitarget CPHD filter. ICASSP 2015: 4055-4059 - [i35]Bastien Pasdeloup, Réda Alami, Vincent Gripon, Michael G. Rabbat:
Toward An Uncertainty Principle For Weighted Graphs. CoRR abs/1503.03291 (2015) - [i34]Farhad Farokhi, Iman Shames, Michael G. Rabbat, Mikael Johansson:
On Reconstructability of Quadratic Utility Functions from the Iterations in Gradient Methods. CoRR abs/1509.05500 (2015) - 2014
- [j22]R. Michael Buehrer, Christopher Robert Anderson, Richard K. Martin, Neal Patwari, Michael G. Rabbat:
Introduction to the Special Issue on Non-Cooperative Localization Networks. IEEE J. Sel. Top. Signal Process. 8(1): 2-4 (2014) - [j21]Pegah Sattari, Maciej Kurant, Animashree Anandkumar, Athina Markopoulou, Michael G. Rabbat:
Active Learning of Multiple Source Multiple Destination Topologies. IEEE Trans. Signal Process. 62(8): 1926-1937 (2014) - [c62]Sean F. Lawlor, Michael G. Rabbat:
Detecting convoys in networks of short-ranged sensors. ACSSC 2014: 717-722 - [c61]Euhanna Ghadimi, André Teixeira, Michael G. Rabbat, Mikael Johansson:
The ADMM algorithm for distributed averaging: Convergence rates and optimal parameter selection. ACSSC 2014: 783-787 - [c60]Sindri Magnússon, P. C. Weeraddana, Michael G. Rabbat, Carlo Fischione:
On the convergence of an alternating direction penalty method for nonconvex problems. ACSSC 2014: 793-797 - [c59]Vincent Gripon, Vitaly Skachek, Michael G. Rabbat:
Sparse binary matrices as efficient associative memories. Allerton 2014: 499-504 - [c58]Michael G. Rabbat, Konstantinos I. Tsianos:
Asynchronous decentralized optimization in heterogeneous systems. CDC 2014: 1125-1130 - [c57]Zhe Yao, Vincent Gripon, Michael G. Rabbat:
A GPU-based associative memory using sparse Neural Networks. HPCS 2014: 688-692 - [c56]Michael G. Rabbat, Vincent Gripon:
Towards a spectral characterization of signals supported on small-world networks. ICASSP 2014: 4793-4797 - [c55]François Leduc-Primeau, Vincent Gripon, Michael G. Rabbat, Warren J. Gross:
Cluster-based associative memories built from unreliable storage. ICASSP 2014: 8370-8374 - [c54]Vitaly Skachek, Michael G. Rabbat:
Subspace synchronization: A network-coding approach to object reconciliation. ISIT 2014: 2301-2305 - [c53]Konstantinos I. Tsianos, Anand D. Sarwate, Michael G. Rabbat:
Tradeoffs for task parallelization in distributed optimization. MLSP 2014: 1-6 - [c52]Babak Fotouhi, Naghmeh Momeni, Michael G. Rabbat:
Generalized Friendship Paradox: An Analytical Approach. SocInfo Workshops 2014: 339-352 - [i33]Konstantinos I. Tsianos, Michael G. Rabbat:
Efficient Distributed Online Prediction and Stochastic Optimization with Approximate Distributed Averaging. CoRR abs/1403.0603 (2014) - [i32]Xiaoran Jiang, Vincent Gripon, Claude Berrou, Michael G. Rabbat:
Storing sequences in binary tournament-based neural networks. CoRR abs/1409.0334 (2014) - [i31]Zhe Yao, Vincent Gripon, Michael G. Rabbat:
Combating Corrupt Messages in Sparse Clustered Associative Memories. CoRR abs/1409.7758 (2014) - [i30]Babak Fotouhi, Naghmeh Momeni, Michael G. Rabbat:
Generalized Friendship Paradox: An Analytical Approach. CoRR abs/1410.0586 (2014) - [i29]Naghmeh Momeni, Michael G. Rabbat:
Measuring the Generalized Friendship Paradox in Networks with Quality-dependent Connectivity. CoRR abs/1411.0556 (2014) - [i28]Ahmet Iscen, Teddy Furon, Vincent Gripon, Michael G. Rabbat, Hervé Jégou:
Memory vectors for similarity search in high-dimensional spaces. CoRR abs/1412.3328 (2014) - 2013
- [j20]Babak Fotouhi, Michael G. Rabbat:
The Effect of Exogenous Inputs and Defiant Agents on Opinion Dynamics With Local and Global Interactions. IEEE J. Sel. Top. Signal Process. 7(2): 347-357 (2013) - [j19]Andrea Edelstein, Michael G. Rabbat:
Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks. IEEE Trans. Mob. Comput. 12(12): 2386-2398 (2013) - [j18]Konstantinos I. Tsianos, Michael G. Rabbat:
Multiscale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks. IEEE Trans. Signal Process. 61(9): 2137-2149 (2013) - [j17]Shaochuan Wu, Michael G. Rabbat:
Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs. IEEE Trans. Signal Process. 61(16): 3959-3971 (2013) - [c51]Jun Ye Yu, Deniz Üstebay, Stéphane Blouin, Michael G. Rabbat, Mark Coates:
Distributed underwater acoustic source localization and tracking. ACSSC 2013: 634-638 - [c50]Vincent Gripon, Vitaly Skachek, Michael G. Rabbat:
Sparse structured associative memories as efficient set-membership data structures. Allerton 2013: 500-505 - [c49]Jun Ye Yu, Michael G. Rabbat:
Performance comparison of randomized gossip, broadcast gossip and collection tree protocol for distributed averaging. CAMSAP 2013: 93-96 - [c48]Pegah Sattari, Maciej Kurant, Animashree Anandkumar, Athina Markopoulou, Michael G. Rabbat:
Active learning of multiple source multiple destination topologies. CISS 2013: 1-6 - [c47]Konstantinos I. Tsianos, Michael G. Rabbat:
Simple iteration-optimal distributed optimization. EUSIPCO 2013: 1-5 - [c46]Konstantinos I. Tsianos, Sean F. Lawlor, Jun Ye Yu, Michael G. Rabbat:
Networked optimization with adaptive communication. GlobalSIP 2013: 579-582 - [c45]Konstantinos I. Tsianos, Michael G. Rabbat:
Consensus-based distributed online prediction and optimization. GlobalSIP 2013: 807-810 - [c44]Vincent Gripon, Michael G. Rabbat:
Reconstructing a graph from path traces. ISIT 2013: 2488-2492 - [c43]Vincent Gripon, Michael G. Rabbat:
Maximum likelihood associative memories. ITW 2013: 1-5 - [i27]Vincent Gripon, Michael G. Rabbat:
Reconstructing a Graph from Path Traces. CoRR abs/1301.6916 (2013) - [i26]Vincent Gripon, Michael G. Rabbat:
Bounds on associative memories. CoRR abs/1301.6917 (2013) - [i25]Zhe Yao, Vincent Gripon, Michael G. Rabbat:
A Massively Parallel Associative Memory Based on Sparse Neural Networks. CoRR abs/1303.7032 (2013) - [i24]Babak Fotouhi, Michael G. Rabbat:
Voter Model with Arbitrary Degree Dependence: Clout, Confidence and Irreversibility. CoRR abs/1308.5121 (2013) - [i23]Babak Fotouhi, Michael G. Rabbat:
Degree Correlation in Scale-Free Graphs. CoRR abs/1308.5169 (2013) - [i22]Zhe Yao, Vincent Gripon, Michael G. Rabbat:
Improving Sparse Associative Memories by Escaping from Bogus Fixed Points. CoRR abs/1308.6003 (2013) - 2012
- [j16]Seyed Salim Tabatabaei, Mark Coates, Michael G. Rabbat:
GANC: Greedy agglomerative normalized cut for graph clustering. Pattern Recognit. 45(2): 831-843 (2012) - [j15]Lipi R. Acharya, Thair Judeh, Zhansheng Duan, Michael G. Rabbat, Dongxiao Zhu:
GSGS: A Computational Approach to Reconstruct Signaling Pathway Structures from Gene Sets. IEEE ACM Trans. Comput. Biol. Bioinform. 9(2): 438-450 (2012) - [j14]Zhe Yao, Philip Mark, Michael G. Rabbat:
Anomaly Detection Using Proximity Graph and PageRank Algorithm. IEEE Trans. Inf. Forensics Secur. 7(4): 1288-1300 (2012) - [j13]Yang Zhao, Neal Patwari, Piyush Agrawal, Michael G. Rabbat:
Directed by Directionality: Benefiting from the Gain Pattern of Active RFID Badges. IEEE Trans. Mob. Comput. 11(5): 865-877 (2012) - [c42]Michael G. Rabbat:
Session MP1b: Signal processing and learning in complex systems (invited). ACSCC 2012: 513-514 - [c41]Michael G. Rabbat, Angelia Nedic:
Convergence properties of normalized random incremental gradient algorithms for least-squares source localization. ACSCC 2012: 1417-1421 - [c40]Babak Fotouhi, Michael G. Rabbat:
Migration in a small world: A network approach to modeling immigration processes. Allerton Conference 2012: 136-143 - [c39]Konstantinos I. Tsianos, Michael G. Rabbat:
Distributed strongly convex optimization. Allerton Conference 2012: 593-600 - [c38]Konstantinos I. Tsianos, Sean F. Lawlor, Michael G. Rabbat:
Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning. Allerton Conference 2012: 1543-1550 - [c37]Babak Fotouhi, Michael G. Rabbat:
Growing a network on a given substrate. Allerton Conference 2012: 2018-2023 - [c36]Konstantinos I. Tsianos, Michael G. Rabbat:
Distributed dual averaging for convex optimization under communication delays. ACC 2012: 1067-1072 - [c35]Bassel Hakoura, Michael G. Rabbat:
Data aggregation in wireless sensor networks: A comparison of collection tree protocols and gossip algorithms. CCECE 2012: 1-4 - [c34]Deniz Üstebay, Michael G. Rabbat:
Efficiently reaching consensus on the largest entries of a vector. CDC 2012: 56-61 - [c33]Konstantinos I. Tsianos, Sean F. Lawlor, Michael G. Rabbat:
Push-Sum Distributed Dual Averaging for convex optimization. CDC 2012: 5453-5458 - [c32]Xiaofan Zhu, Michael G. Rabbat:
Graph spectral compressed sensing for sensor networks. ICASSP 2012: 2865-2868 - [c31]Xiaofan Zhu, Michael G. Rabbat:
Approximating signals supported on graphs. ICASSP 2012: 3921-3924 - [c30]Vincent Gripon, Vitaly Skachek, Warren J. Gross, Michael G. Rabbat:
Random clique codes. ISTC 2012: 121-125 - [c29]Vincent Gripon, Michael G. Rabbat, Vitaly Skachek, Warren J. Gross:
Compressing multisets using tries. ITW 2012: 642-646 - [c28]Konstantinos I. Tsianos, Sean F. Lawlor, Michael G. Rabbat:
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization. NIPS 2012: 1952-1960 - [c27]Deniz Üstebay, Michael G. Rabbat:
TOP-K selective gossip. SPAWC 2012: 505-509 - [c26]Michael G. Rabbat:
Bounded confidence opinion dynamics with network constraints and localized distributed averaging. SSP 2012: 632-635 - [i21]Andrea Edelstein, Michael G. Rabbat:
Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks. CoRR abs/1207.1137 (2012) - [i20]Konstantinos I. Tsianos, Michael G. Rabbat:
Distributed Strongly Convex Optimization. CoRR abs/1207.3031 (2012) - [i19]Konstantinos I. Tsianos, Michael G. Rabbat:
The Impact of Communication Delays on Distributed Consensus Algorithms. CoRR abs/1207.5839 (2012) - [i18]Babak Fotouhi, Michael G. Rabbat:
Growing a Network on a Given Substrate. CoRR abs/1207.5847 (2012) - [i17]Babak Fotouhi, Michael G. Rabbat:
Migration in a Small World: A Network Approach to Modeling Immigration Processes. CoRR abs/1207.5849 (2012) - [i16]Babak Fotouhi, Michael G. Rabbat:
Dynamics of Infuence on Hierarchical Structures: Towards the Statistical Mechanics of Social Class Struggle. CoRR abs/1207.7251 (2012) - [i15]Vincent Gripon, Vitaly Skachek, Michael G. Rabbat:
Forwarding Without Repeating: Efficient Rumor Spreading in Bounded-Degree Graphs. CoRR abs/1208.2936 (2012) - [i14]Babak Fotouhi, Michael G. Rabbat:
The Effect of Exogenous Inputs and Defiant Agents on Opinion Dynamics with Local and Global Interactions. CoRR abs/1208.3252 (2012) - [i13]Shaochuan Wu, Michael G. Rabbat:
Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs. CoRR abs/1208.4895 (2012) - [i12]Konstantinos I. Tsianos, Sean F. Lawlor, Michael G. Rabbat:
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization. CoRR abs/1209.1076 (2012) - [i11]Babak Fotouhi, Michael G. Rabbat:
Network Growth with Arbitrary Initial Conditions: Analytical Results for Uniform and Preferential Attachment. CoRR abs/1212.0435 (2012) - [i10]Pegah Sattari, Maciej Kurant, Animashree Anandkumar, Athina Markopoulou, Michael G. Rabbat:
Active Learning of Multiple Source Multiple Destination Topologies. CoRR abs/1212.2310 (2012) - 2011
- [j12]Frederic Thouin, Mark Coates, Michael G. Rabbat:
Large scale probabilistic available bandwidth estimation. Comput. Networks 55(9): 2065-2078 (2011) - [j11]Yvan Pointurier, Mark Coates, Michael G. Rabbat:
Cross-Layer Monitoring in Transparent Optical Networks. JOCN 3(3): 189-198 (2011) - [j10]Deniz Üstebay, Rui M. Castro, Michael G. Rabbat:
Efficient Decentralized Approximation via Selective Gossip. IEEE J. Sel. Top. Signal Process. 5(4): 805-816 (2011) - [c25]Konstantinos I. Tsianos, Michael G. Rabbat:
Distributed consensus and optimization under communication delays. Allerton 2011: 974-982 - [c24]Ali Daher, Michael G. Rabbat, Vincent K. N. Lau:
Local silencing rules for randomized gossip. DCOSS 2011: 1-8 - [c23]Deniz Üstebay, Mark Coates, Michael G. Rabbat:
Distributed auxiliary particle filters using selective gossip. ICASSP 2011: 3296-3299 - [c22]Andrea Edelstein, Xi Chen, Yunpeng Li, Michael G. Rabbat:
RSS-based node localization in the presence of attenuating objects. ICASSP 2011: 3528-3531 - [c21]Xi Chen, Andrea Edelstein, Yunpeng Li, Mark Coates, Michael G. Rabbat, Aidong Men:
Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements. IPSN 2011: 342-353 - [c20]Wen Qin, Michael G. Rabbat, Bo Yang:
A correlation model for shadow fading in multi-hop wireless networks. SpringSim (ANSS) 2011: 100-104 - [i9]Seyed Salim Tabatabaei, Mark Coates, Michael G. Rabbat:
GANC: Greedy Agglomerative Normalized Cut. CoRR abs/1105.0974 (2011) - 2010
- [j9]Alexandros G. Dimakis, Soummya Kar, José M. F. Moura, Michael G. Rabbat, Anna Scaglione:
Gossip Algorithms for Distributed Signal Processing. Proc. IEEE 98(11): 1847-1864 (2010) - [j8]Boris N. Oreshkin, Mark Coates, Michael G. Rabbat:
Optimization and analysis of distributed averaging with short node memory. IEEE Trans. Signal Process. 58(5): 2850-2865 (2010) - [j7]Deniz Üstebay, Boris N. Oreshkin, Mark Coates, Michael G. Rabbat:
Greedy gossip with eavesdropping. IEEE Trans. Signal Process. 58(7): 3765-3776 (2010) - [c19]Konstantinos I. Tsianos, Michael G. Rabbat:
Fast Decentralized Averaging via Multi-scale Gossip. DCOSS 2010: 320-333 - [i8]Frederic Thouin, Mark Coates, Michael G. Rabbat:
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning. CoRR abs/1001.1009 (2010) - [i7]Alexandros G. Dimakis, Soummya Kar, José M. F. Moura, Michael G. Rabbat, Anna Scaglione:
Gossip Algorithms for Distributed Signal Processing. CoRR abs/1003.5309 (2010) - [i6]Frederic Thouin, Mark Coates, Michael G. Rabbat:
Large scale probabilistic available bandwidth estimation. CoRR abs/1007.0730 (2010) - [i5]Frederic Thouin, Mark Coates, Michael G. Rabbat:
Real-Time Multi-path Tracking of Probabilistic Available Bandwidth. CoRR abs/1010.1524 (2010) - [i4]Konstantinos I. Tsianos, Michael G. Rabbat:
Multiscale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks. CoRR abs/1011.2235 (2010)
2000 – 2009
- 2009
- [j6]Hong Li, Lorne Mason, Michael G. Rabbat:
Distributed adaptive diverse routing for voice-over-IP in service overlay networks. IEEE Trans. Netw. Serv. Manag. 6(3): 175-189 (2009) - [c18]Boris N. Oreshkin, Mark J. Coates, Michael G. Rabbat:
Optimization and analysis of distributed averaging with memory. Allerton 2009: 347-354 - [c17]Mohammad A. Kanso, Michael G. Rabbat:
Compressed RF Tomography for Wireless Sensor Networks: Centralized and Decentralized Approaches. DCOSS 2009: 173-186 - [c16]Deniz Üstebay, Boris N. Oreshkin, Mark Coates, Michael G. Rabbat:
Multi-hop Greedy Gossip with Eavesdropping. FUSION 2009: 140-145 - [c15]Deniz Üstebay, Boris N. Oreshkin, Mark Coates, Michael G. Rabbat:
The speed of greed: Characterizing myopic gossip through network voracity. ICASSP 2009: 3665-3668 - [i3]Boris N. Oreshkin, Mark Coates, Michael G. Rabbat:
Optimization and Analysis of Distributed Averaging with Short Node Memory. CoRR abs/0903.3537 (2009) - [i2]Deniz Üstebay, Boris N. Oreshkin, Mark Coates, Michael G. Rabbat:
Greedy Gossip with Eavesdropping. CoRR abs/0909.1830 (2009) - 2008
- [j5]Jarvis D. Haupt, Waheed U. Bajwa, Michael G. Rabbat, Robert D. Nowak:
Compressed Sensing for Networked Data. IEEE Signal Process. Mag. 25(2): 92-101 (2008) - [j4]Michael G. Rabbat, Mário A. T. Figueiredo, Robert D. Nowak:
Network Inference From Co-Occurrences. IEEE Trans. Inf. Theory 54(9): 4053-4068 (2008) - [j3]Tuncer C. Aysal, Mark Coates, Michael G. Rabbat:
Distributed Average Consensus With Dithered Quantization. IEEE Trans. Signal Process. 56(10-1): 4905-4918 (2008) - [c14]Xiaojin Zhu, Andrew B. Goldberg, Michael G. Rabbat, Robert D. Nowak:
Learning Bigrams from Unigrams. ACL 2008: 656-664 - [c13]Deniz Üstebay, Boris N. Oreshkin, Mark Coates, Michael G. Rabbat:
Rates of convergence for greedy gossip with eavesdropping. Allerton 2008: 367-374 - [c12]Deniz Üstebay, Mark Coates, Michael G. Rabbat:
Greedy gossip with eavesdropping. ISWPC 2008: 759-763 - [c11]Hong Li, Lorne Mason, Michael G. Rabbat:
Learning Minimum Delay Paths in Service Overlay Networks. NCA 2008: 271-274 - 2007
- [c10]Michael G. Rabbat, Mário A. T. Figueiredo, Robert D. Nowak:
Genomic Network Tomography. ICASSP (1) 2007: 373-376 - [c9]Mark Coates, Yvan Pointurier, Michael G. Rabbat:
Compressed network monitoring for ip and all-optical networks. Internet Measurement Conference 2007: 241-252 - 2006
- [j2]Michael G. Rabbat, Mark Coates, Robert D. Nowak:
Multiple-Source Internet Tomography. IEEE J. Sel. Areas Commun. 24(12): 2221-2234 (2006) - [c8]Michael G. Rabbat, Jarvis D. Haupt, Aarti Singh, Robert D. Nowak:
Decentralized compression and predistribution via randomized gossiping. IPSN 2006: 51-59 - [c7]Michael G. Rabbat, Mário A. T. Figueiredo, Robert D. Nowak:
Inferring Network Structure from Co-Occurrences. NIPS 2006: 1105-1112 - [i1]Michael G. Rabbat, Mário A. T. Figueiredo, Robert D. Nowak:
Network Inference from Co-Occurrences. CoRR abs/cs/0605100 (2006) - 2005
- [j1]Michael G. Rabbat, Robert D. Nowak:
Quantized incremental algorithms for distributed optimization. IEEE J. Sel. Areas Commun. 23(4): 798-808 (2005) - [c6]Michael G. Rabbat, John R. Treichler, Sally L. Wood, Michael G. Larimore:
Understanding the topology of a telephone network via internally-sensed network tomography. ICASSP (3) 2005: 977-980 - [c5]Michael G. Rabbat, Robert D. Nowak, James A. Bucklew:
Robust decentralized source localization via averaging. ICASSP (5) 2005: 1057-1060 - 2004
- [c4]Michael G. Rabbat, Robert D. Nowak:
Decentralized source localization and tracking [wireless sensor networks]. ICASSP (3) 2004: 921-924 - [c3]Michael G. Rabbat, Robert D. Nowak, Mark Coates:
Multiple Source, Multiple Destination Network Tomography. INFOCOM 2004: 1628-1639 - [c2]Michael G. Rabbat, Robert D. Nowak:
Distributed optimization in sensor networks. IPSN 2004: 20-27 - 2003
- [c1]Mark Coates, Michael G. Rabbat, Robert D. Nowak:
Merging logical topologies using end-to-end measurements. Internet Measurement Conference 2003: 192-203
Coauthor Index
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