Merging logical topologies using end-to-end measurements. - Mark Coates; Michael G. Rabbat; Robert D. Nowak (Internet Measurement Conference, 2003)

Decentralized source localization and tracking [wireless sensor networks]. - Michael G. Rabbat; Robert D. Nowak (ICASSP (3), 2004)

Multiple Source, Multiple Destination Network Tomography. - Michael G. Rabbat; Robert D. Nowak; Mark Coates (INFOCOM, 2004)

Distributed optimization in sensor networks. - Michael G. Rabbat; Robert D. Nowak (IPSN, 2004)

Robust decentralized source localization via averaging. - Michael G. Rabbat; Robert D. Nowak; James A. Bucklew (ICASSP (5), 2005)

Understanding the topology of a telephone network via internally-sensed network tomography. - Michael G. Rabbat; John R. Treichler; Sally L. Wood; Michael G. Larimore (ICASSP (3), 2005)

Quantized incremental algorithms for distributed optimization. - Michael G. Rabbat; Robert D. Nowak (IEEE J. Sel. Areas Commun., 2005)

Decentralized compression and predistribution via randomized gossiping. - Michael G. Rabbat; Jarvis D. Haupt; Aarti Singh; Robert D. Nowak (IPSN, 2006)

Inferring Network Structure from Co-Occurrences. - Michael G. Rabbat; Mário A. T. Figueiredo; Robert D. Nowak (NIPS, 2006)

Multiple-Source Internet Tomography. - Michael G. Rabbat; Mark Coates; Robert D. Nowak (IEEE J. Sel. Areas Commun., 2006)

Genomic Network Tomography. - Michael G. Rabbat; Mário A. T. Figueiredo; Robert D. Nowak (ICASSP (1), 2007)

Compressed network monitoring for ip and all-optical networks. - Mark Coates; Yvan Pointurier; Michael G. Rabbat (Internet Measurement Conference, 2007)

Learning Bigrams from Unigrams. - Xiaojin Zhu 001; Andrew B. Goldberg; Michael G. Rabbat; Robert D. Nowak (ACL, 2008)

Rates of convergence for greedy gossip with eavesdropping. - Deniz Üstebay; Boris N. Oreshkin; Mark Coates; Michael G. Rabbat (Allerton, 2008)

Greedy gossip with eavesdropping. - Deniz Üstebay; Mark Coates; Michael G. Rabbat (ISWPC, 2008)

Learning Minimum Delay Paths in Service Overlay Networks. - Hong Li; Lorne Mason; Michael G. Rabbat (NCA, 2008)

Compressed Sensing for Networked Data. - Jarvis D. Haupt; Waheed U. Bajwa; Michael G. Rabbat; Robert D. Nowak (IEEE Signal Process. Mag., 2008)

Network Inference From Co-Occurrences. - Michael G. Rabbat; Mário A. T. Figueiredo; Robert D. Nowak (IEEE Trans. Inf. Theory, 2008)

Distributed Average Consensus With Dithered Quantization. - Tuncer C. Aysal; Mark Coates; Michael G. Rabbat (IEEE Trans. Signal Process., 2008)

Optimization and analysis of distributed averaging with memory. - Boris N. Oreshkin; Mark J. Coates; Michael G. Rabbat (Allerton, 2009)

Compressed RF Tomography for Wireless Sensor Networks: Centralized and Decentralized Approaches. - Mohammad A. Kanso; Michael G. Rabbat (DCOSS, 2009)

Multi-hop Greedy Gossip with Eavesdropping. - Deniz Üstebay; Boris N. Oreshkin; Mark Coates; Michael G. Rabbat (FUSION, 2009)

The speed of greed: Characterizing myopic gossip through network voracity. - Deniz Üstebay; Boris N. Oreshkin; Mark Coates; Michael G. Rabbat (ICASSP, 2009)

Distributed adaptive diverse routing for voice-over-IP in service overlay networks. - Hong Li; Lorne Mason; Michael G. Rabbat (IEEE Trans. Netw. Serv. Manag., 2009)

Fast Decentralized Averaging via Multi-scale Gossip. - Konstantinos I. Tsianos; Michael G. Rabbat (DCOSS, 2010)

Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning - Frederic Thouin; Mark Coates; Michael G. Rabbat (CoRR, 2010)

Real-Time Multi-path Tracking of Probabilistic Available Bandwidth - Frederic Thouin; Mark Coates; Michael G. Rabbat (CoRR, 2010)

Gossip Algorithms for Distributed Signal Processing. - Alexandros G. Dimakis; Soummya Kar; José M. F. Moura; Michael G. Rabbat; Anna Scaglione (Proc. IEEE, 2010)

Optimization and analysis of distributed averaging with short node memory. - Boris N. Oreshkin; Mark Coates; Michael G. Rabbat (IEEE Trans. Signal Process., 2010)

Greedy gossip with eavesdropping. - Deniz Üstebay; Boris N. Oreshkin; Mark Coates; Michael G. Rabbat (IEEE Trans. Signal Process., 2010)

Distributed consensus and optimization under communication delays. - Konstantinos I. Tsianos; Michael G. Rabbat (Allerton, 2011)

Local silencing rules for randomized gossip. - Ali Daher; Michael G. Rabbat; Vincent K. N. Lau (DCOSS, 2011)

RSS-based node localization in the presence of attenuating objects. - Andrea Edelstein; Xi Chen 042; Yunpeng Li 001; Michael G. Rabbat (ICASSP, 2011)

Distributed auxiliary particle filters using selective gossip. - Deniz Üstebay; Mark Coates; Michael G. Rabbat (ICASSP, 2011)

Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements. - Xi Chen 042; Andrea Edelstein; Yunpeng Li 001; Mark Coates; Michael G. Rabbat; Aidong Men (IPSN, 2011)

A correlation model for shadow fading in multi-hop wireless networks. - Wen Qin; Michael G. Rabbat; Bo Yang (SpringSim (ANSS), 2011)

Large scale probabilistic available bandwidth estimation. - Frederic Thouin; Mark Coates; Michael G. Rabbat (Comput. Networks, 2011)

GANC: Greedy Agglomerative Normalized Cut - Seyed Salim Tabatabaei; Mark Coates; Michael G. Rabbat (CoRR, 2011)

Cross-Layer Monitoring in Transparent Optical Networks. - Yvan Pointurier; Mark Coates; Michael G. Rabbat (JOCN, 2011)

Efficient Decentralized Approximation via Selective Gossip. - Deniz Üstebay; Rui M. Castro; Michael G. Rabbat (IEEE J. Sel. Top. Signal Process., 2011)

Session MP1b: Signal processing and learning in complex systems (invited). - Michael G. Rabbat (ACSCC, 2012)

Convergence properties of normalized random incremental gradient algorithms for least-squares source localization. - Michael G. Rabbat; Angelia Nedic (ACSCC, 2012)

Migration in a small world: A network approach to modeling immigration processes. - Babak Fotouhi; Michael G. Rabbat (Allerton Conference, 2012)

Growing a network on a given substrate. - Babak Fotouhi; Michael G. Rabbat (Allerton Conference, 2012)

Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning. - Konstantinos I. Tsianos; Sean F. Lawlor; Michael G. Rabbat (Allerton Conference, 2012)

Distributed strongly convex optimization. - Konstantinos I. Tsianos; Michael G. Rabbat (Allerton Conference, 2012)

Distributed dual averaging for convex optimization under communication delays. - Konstantinos I. Tsianos; Michael G. Rabbat (ACC, 2012)

Data aggregation in wireless sensor networks: A comparison of collection tree protocols and gossip algorithms. - Bassel Hakoura; Michael G. Rabbat (CCECE, 2012)

Push-Sum Distributed Dual Averaging for convex optimization. - Konstantinos I. Tsianos; Sean F. Lawlor; Michael G. Rabbat (CDC, 2012)

Efficiently reaching consensus on the largest entries of a vector. - Deniz Üstebay; Michael G. Rabbat (CDC, 2012)

Graph spectral compressed sensing for sensor networks. - Xiaofan Zhu; Michael G. Rabbat (ICASSP, 2012)

Approximating signals supported on graphs. - Xiaofan Zhu; Michael G. Rabbat (ICASSP, 2012)

Random clique codes. - Vincent Gripon; Vitaly Skachek; Warren J. Gross; Michael G. Rabbat (ISTC, 2012)

Compressing multisets using tries. - Vincent Gripon; Michael G. Rabbat; Vitaly Skachek; Warren J. Gross (ITW, 2012)

Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization. - Konstantinos I. Tsianos; Sean F. Lawlor; Michael G. Rabbat (NIPS, 2012)

TOP-K selective gossip. - Deniz Üstebay; Michael G. Rabbat (SPAWC, 2012)

Bounded confidence opinion dynamics with network constraints and localized distributed averaging. - Michael G. Rabbat (SSP, 2012)

The Impact of Communication Delays on Distributed Consensus Algorithms - Konstantinos I. Tsianos; Michael G. Rabbat (CoRR, 2012)

Dynamics of Infuence on Hierarchical Structures: Towards the Statistical Mechanics of Social Class Struggle - Babak Fotouhi; Michael G. Rabbat (CoRR, 2012)

Forwarding Without Repeating: Efficient Rumor Spreading in Bounded-Degree Graphs - Vincent Gripon; Vitaly Skachek; Michael G. Rabbat (CoRR, 2012)

Network Growth with Arbitrary Initial Conditions: Analytical Results for Uniform and Preferential Attachment - Babak Fotouhi; Michael G. Rabbat (CoRR, 2012)

GANC: Greedy agglomerative normalized cut for graph clustering. - Seyed Salim Tabatabaei; Mark Coates; Michael G. Rabbat (Pattern Recognit., 2012)

GSGS: A Computational Approach to Reconstruct Signaling Pathway Structures from Gene Sets. - Lipi R. Acharya; Thair Judeh; Zhansheng Duan; Michael G. Rabbat; Dongxiao Zhu (IEEE ACM Trans. Comput. Biol. Bioinform., 2012)

Anomaly Detection Using Proximity Graph and PageRank Algorithm. - Zhe Yao; Philip Mark; Michael G. Rabbat (IEEE Trans. Inf. Forensics Secur., 2012)

Directed by Directionality: Benefiting from the Gain Pattern of Active RFID Badges. - Yang Zhao 020; Neal Patwari; Piyush Agrawal; Michael G. Rabbat (IEEE Trans. Mob. Comput., 2012)

Distributed underwater acoustic source localization and tracking. - Jun Ye Yu; Deniz Üstebay; Stéphane Blouin; Michael G. Rabbat; Mark Coates (ACSSC, 2013)

Sparse structured associative memories as efficient set-membership data structures. - Vincent Gripon; Vitaly Skachek; Michael G. Rabbat (Allerton, 2013)

Performance comparison of randomized gossip, broadcast gossip and collection tree protocol for distributed averaging. - Jun Ye Yu; Michael G. Rabbat (CAMSAP, 2013)

Simple iteration-optimal distributed optimization. - Konstantinos I. Tsianos; Michael G. Rabbat (EUSIPCO, 2013)

Networked optimization with adaptive communication. - Konstantinos I. Tsianos; Sean F. Lawlor; Jun Ye Yu; Michael G. Rabbat (GlobalSIP, 2013)

Consensus-based distributed online prediction and optimization. - Konstantinos I. Tsianos; Michael G. Rabbat (GlobalSIP, 2013)

Reconstructing a graph from path traces. - Vincent Gripon; Michael G. Rabbat (ISIT, 2013)

Maximum likelihood associative memories. - Vincent Gripon; Michael G. Rabbat (ITW, 2013)

Voter Model with Arbitrary Degree Dependence: Clout, Confidence and Irreversibility. - Babak Fotouhi; Michael G. Rabbat (CoRR, 2013)

Degree Correlation in Scale-Free Graphs. - Babak Fotouhi; Michael G. Rabbat (CoRR, 2013)

Improving Sparse Associative Memories by Escaping from Bogus Fixed Points. - Zhe Yao; Vincent Gripon; Michael G. Rabbat (CoRR, 2013)

Bounds on associative memories - Vincent Gripon; Michael G. Rabbat (CoRR, 2013)

A Massively Parallel Associative Memory Based on Sparse Neural Networks - Zhe Yao; Vincent Gripon; Michael G. Rabbat (CoRR, 2013)

The Effect of Exogenous Inputs and Defiant Agents on Opinion Dynamics With Local and Global Interactions. - Babak Fotouhi; Michael G. Rabbat (IEEE J. Sel. Top. Signal Process., 2013)

Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks. - Andrea Edelstein; Michael G. Rabbat (IEEE Trans. Mob. Comput., 2013)

Multiscale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks. - Konstantinos I. Tsianos; Michael G. Rabbat (IEEE Trans. Signal Process., 2013)

Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs. - Shaochuan Wu; Michael G. Rabbat (IEEE Trans. Signal Process., 2013)

The ADMM algorithm for distributed averaging: Convergence rates and optimal parameter selection. - Euhanna Ghadimi; André Teixeira 001; Michael G. Rabbat; Mikael Johansson 001 (ACSSC, 2014)

Detecting convoys in networks of short-ranged sensors. - Sean F. Lawlor; Michael G. Rabbat (ACSSC, 2014)

On the convergence of an alternating direction penalty method for nonconvex problems. - Sindri Magnússon; P. C. Weeraddana; Michael G. Rabbat; Carlo Fischione (ACSSC, 2014)

Sparse binary matrices as efficient associative memories. - Vincent Gripon; Vitaly Skachek; Michael G. Rabbat (Allerton, 2014)

Asynchronous decentralized optimization in heterogeneous systems. - Michael G. Rabbat; Konstantinos I. Tsianos (CDC, 2014)

A GPU-based associative memory using sparse Neural Networks. - Zhe Yao; Vincent Gripon; Michael G. Rabbat (HPCS, 2014)

Cluster-based associative memories built from unreliable storage. - François Leduc-Primeau; Vincent Gripon; Michael G. Rabbat; Warren J. Gross (ICASSP, 2014)

Towards a spectral characterization of signals supported on small-world networks. - Michael G. Rabbat; Vincent Gripon (ICASSP, 2014)

Subspace synchronization: A network-coding approach to object reconciliation. - Vitaly Skachek; Michael G. Rabbat (ISIT, 2014)

Tradeoffs for task parallelization in distributed optimization. - Konstantinos I. Tsianos; Anand D. Sarwate; Michael G. Rabbat (MLSP, 2014)

Generalized Friendship Paradox: An Analytical Approach. - Babak Fotouhi; Naghmeh Momeni; Michael G. Rabbat (SocInfo Workshops, 2014)

Combating Corrupt Messages in Sparse Clustered Associative Memories. - Zhe Yao; Vincent Gripon; Michael G. Rabbat (CoRR, 2014)

Introduction to the Special Issue on Non-Cooperative Localization Networks. - R. Michael Buehrer; Christopher Robert Anderson; Richard K. Martin; Neal Patwari; Michael G. Rabbat (IEEE J. Sel. Top. Signal Process., 2014)

Active Learning of Multiple Source Multiple Destination Topologies. - Pegah Sattari; Maciej Kurant; Animashree Anandkumar; Athina Markopoulou; Michael G. Rabbat (IEEE Trans. Signal Process., 2014)

Graph reconstruction from the observation of diffused signals. - Bastien Pasdeloup; Michael G. Rabbat; Vincent Gripon; Dominique Pastor; Grégoire Mercier (Allerton, 2015)

Particle weight approximation with clustering for gossip-based distributed particle filters. - Chon-Wang Chao; Michael G. Rabbat; Stéphane Blouin (CAMSAP, 2015)

Multi-agent mirror descent for decentralized stochastic optimization. - Michael G. Rabbat (CAMSAP, 2015)

Performance investigation on constraint sufficient statistics distributed particle filter. - Jun Ye Yu; Michael G. Rabbat; Mark Coates; Stéphane Blouin (CCECE, 2015)

Measuring the Generalized Friendship Paradox in Networks with Quality-Dependent Connectivity. - Naghmeh Momeni; Michael G. Rabbat (CompleNet, 2015)

Toward an uncertainty principle for weighted graphs. - Bastien Pasdeloup; Réda Alami; Vincent Gripon; Michael G. Rabbat (EUSIPCO, 2015)

General solution and approximate implementation of the multisensor multitarget CPHD filter. - Santosh Nannuru; Mark Coates; Michael G. Rabbat; Stéphane Blouin (ICASSP, 2015)

Error Propagation in Gossip-Based Distributed Particle Filters. - Syamantak Datta Gupta; Mark Coates; Michael G. Rabbat (IEEE Trans. Signal Inf. Process. over Networks, 2015)

Totally asynchronous distributed estimation of eigenvector centrality in digraphs with application to the PageRank problem. - Themistoklis Charalambous; Christoforos N. Hadjicostis; Michael G. Rabbat; Mikael Johansson 001 (CDC, 2016)

Efficient Large-Scale Similarity Search Using Matrix Factorization. - Ahmet Iscen; Michael G. Rabbat; Teddy Furon (CVPR, 2016)

Graph Laplacian distributed particle filtering. - Michael G. Rabbat; Mark Coates; Stéphane Blouin (EUSIPCO, 2016)

Distributed multi-sensor CPHD filter using pairwise gossiping. - Jun Ye Yu; Mark Coates; Michael G. Rabbat (ICASSP, 2016)

Estimation of time-varying mixture models: An application to traffic estimation. - Sean F. Lawlor; Michael G. Rabbat (SSP, 2016)

Inferring network properties from fixed-choice design with strong and weak ties. - Naghmeh Momeni; Michael G. Rabbat (SSP, 2016)

On reconstructability of quadratic utility functions from the iterations in gradient methods. - Farhad Farokhi; Iman Shames; Michael G. Rabbat; Mikael Johansson 001 (Autom., 2016)

Qualities and Inequalities in Online Social Networks through the Lens of the Generalized Friendship Paradox. - Naghmeh Momeni; Michael G. Rabbat (CoRR, 2016)

Characterization and inference of weighted graph topologies from observations of diffused signals. - Bastien Pasdeloup; Vincent Gripon; Grégoire Mercier; Dominique Pastor; Michael G. Rabbat (CoRR, 2016)

A Distributed Particle Filter for Bearings-Only Tracking on Spherical Surfaces. - Jun Ye Yu; Mark J. Coates; Michael G. Rabbat; Stéphane Blouin (IEEE Signal Process. Lett., 2016)

Multisensor CPHD filter. - Santosh Nannuru; Stéphane Blouin; Mark Coates; Michael G. Rabbat (IEEE Trans. Aerosp. Electron. Syst., 2016)

Distributed Finite-Time Computation of Digraph Parameters: Left-Eigenvector, Out-Degree and Spectrum. - Themistoklis Charalambous; Michael G. Rabbat; Mikael Johansson 001; Christoforos N. Hadjicostis (IEEE Trans. Control. Netw. Syst., 2016)

On the Convergence of Alternating Direction Lagrangian Methods for Nonconvex Structured Optimization Problems. - Sindri Magnússon; Pradeep Chathuranga Weeraddana; Michael G. Rabbat; Carlo Fischione (IEEE Trans. Control. Netw. Syst., 2016)

Storing Sequences in Binary Tournament-Based Neural Networks. - Xiaoran Jiang; Vincent Gripon; Claude Berrou; Michael G. Rabbat (IEEE Trans. Neural Networks Learn. Syst., 2016)

Detecting Convoys Using License Plate Recognition Data. - Sean F. Lawlor; Timothy Sider; Naveen Eluru; Marianne Hatzopoulou; Michael G. Rabbat (IEEE Trans. Signal Inf. Process. over Networks, 2016)

Efficient Distributed Online Prediction and Stochastic Optimization With Approximate Distributed Averaging. - Konstantinos I. Tsianos; Michael G. Rabbat (IEEE Trans. Signal Inf. Process. over Networks, 2016)

Fault-Tolerant Associative Memories Based on c-Partite Graphs. - François Leduc-Primeau; Vincent Gripon; Michael G. Rabbat; Warren J. Gross (IEEE Trans. Signal Process., 2016)

Algorithms for the multi-sensor assignment problem in the δ-generalized labeled multi-Bernoulli filter. - Jun Ye Yu; Augustin-Alexandru Saucan; Mark Coates; Michael G. Rabbat (CAMSAP, 2017)

An empirical comparison of multi-agent optimization algorithms. - Mahmoud Assran; Michael G. Rabbat (GlobalSIP, 2017)

Inferring sparse graphs from smooth signals with theoretical guarantees. - Michael G. Rabbat (ICASSP, 2017)

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. - Pascal Frossard; Pier Luigi Dragotti; Antonio Ortega; Michael G. Rabbat; Alejandro Ribeiro (IEEE J. Sel. Top. Signal Process., 2017)

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. - Pascal Frossard; Pier Luigi Dragotti; Antonio Ortega; Michael G. Rabbat; Alejandro Ribeiro (IEEE Trans. Signal Inf. Process. over Networks, 2017)

Inferring Structural Characteristics of Networks With Strong and Weak Ties From Fixed-Choice Surveys. - Naghmeh Momeni; Michael G. Rabbat (IEEE Trans. Signal Inf. Process. over Networks, 2017)

Time-Varying Mixtures of Markov Chains: An Application to Road Traffic Modeling. - Sean F. Lawlor; Michael G. Rabbat (IEEE Trans. Signal Process., 2017)

A Multisensor Multi-Bernoulli Filter. - Augustin-Alexandru Saucan; Mark J. Coates; Michael G. Rabbat (IEEE Trans. Signal Process., 2017)

A Graph-CNN for 3D Point Cloud Classification. - Yingxue Zhang 005; Michael G. Rabbat (ICASSP, 2018)

Asynchronous Subgradient-Push. - Mahmoud Assran; Michael G. Rabbat (CoRR, 2018)

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. - 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; et al. (CoRR, 2018)

Effectiveness of Alter Sampling in Social Networks. - Naghmeh Momeni; Michael G. Rabbat (CoRR, 2018)

Temporal evolution of the degree distribution of alters in growing networks. - Babak Fotouhi; Michael G. Rabbat (Netw. Sci., 2018)

Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization. - Angelia Nedic; Alex Olshevsky; Michael G. Rabbat (Proc. IEEE, 2018)

Memory Vectors for Similarity Search in High-Dimensional Spaces. - Ahmet Iscen; Teddy Furon; Vincent Gripon; Michael G. Rabbat; Hervé Jégou (IEEE Trans. Big Data, 2018)

Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals. - Bastien Pasdeloup; Vincent Gripon; Grégoire Mercier; Dominique Pastor; Michael G. Rabbat (IEEE Trans. Signal Inf. Process. over Networks, 2018)

Provably Accelerated Randomized Gossip Algorithms. - Nicolas Loizou; Michael G. Rabbat; Peter Richtárik (ICASSP, 2019)

Stochastic Gradient Push for Distributed Deep Learning. - Mahmoud Assran; Nicolas Loizou; Nicolas Ballas; Michael G. Rabbat (ICML, 2019)

TarMAC: Targeted Multi-Agent Communication. - Abhishek Das; Théophile Gervet; Joshua Romoff; Dhruv Batra; Devi Parikh; Mike Rabbat; Joelle Pineau (ICML, 2019)

Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. - Mahmoud Assran; Joshua Romoff; Nicolas Ballas; Joelle Pineau; Mike Rabbat (NeurIPS, 2019)

MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions. - Viswanath Sivakumar; Tim Rocktäschel; Alexander H. Miller; Heinrich Küttler; Nantas Nardelli; Mike Rabbat; Joelle Pineau; Sebastian Riedel 001 (CoRR, 2019)

Learning Graphs From Data: A Signal Representation Perspective. - Xiaowen Dong 001; Dorina Thanou; Michael G. Rabbat; Pascal Frossard (IEEE Signal Process. Mag., 2019)

Improved Bounds for Max Consensus in Wireless Networks. - Aida Nowzari; Michael G. Rabbat (IEEE Trans. Signal Inf. Process. over Networks, 2019)

Graph-Based Compression for Distributed Particle Filters. - Jun Ye Yu; Mark J. Coates; Michael G. Rabbat (IEEE Trans. Signal Inf. Process. over Networks, 2019)

Stability of Decentralized Gradient Descent in Open Multi-Agent Systems. - Julien M. Hendrickx; Michael G. Rabbat (CDC, 2020)

Lookahead Converges to Stationary Points of Smooth Non-convex Functions. - Jianyu Wang; Vinayak Tantia; Nicolas Ballas; Michael G. Rabbat (ICASSP, 2020)

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum. - Jianyu Wang; Vinayak Tantia; Nicolas Ballas; Michael G. Rabbat (ICLR, 2020)

On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings. - Mahmoud Assran; Mike Rabbat (ICML, 2020)

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. - 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 (CoRR, 2020)

Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations. - Mahmoud Assran; Nicolas Ballas; Lluís Castrejón; Michael G. Rabbat (CoRR, 2020)

A Closer Look at Codistillation for Distributed Training. - Shagun Sodhani; Olivier Delalleau; Mahmoud Assran; Koustuv Sinha; Nicolas Ballas; Michael G. Rabbat (CoRR, 2020)

CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. - 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 (CoRR, 2020)

Advances in Asynchronous Parallel and Distributed Optimization. - Mahmoud Assran; Arda Aytekin; Hamid Reza Feyzmahdavian; Mikael Johansson 001; Michael G. Rabbat (Proc. IEEE, 2020)

Optimization for Data-Driven Learning and Control. - Usman A. Khan; Waheed U. Bajwa; Angelia Nedic; Michael G. Rabbat; Ali H. Sayed (Proc. IEEE, 2020)

Learning with Gradient Descent and Weakly Convex Losses. - Dominic Richards; Mike Rabbat (AISTATS, 2021)

Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples. - Mahmoud Assran; Mathilde Caron; Ishan Misra; Piotr Bojanowski; Armand Joulin; Nicolas Ballas; Michael G. Rabbat (ICCV, 2021)

Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery. - 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 (MLSys, 2021)

Stochastic Polyak Stepsize with a Moving Target. - Robert M. Gower; Aaron Defazio; Michael G. Rabbat (CoRR, 2021)

Trade-offs of Local SGD at Scale: An Empirical Study. - Jose Javier Gonzalez Ortiz; Jonathan Frankle; Mike Rabbat; Ari S. Morcos; Nicolas Ballas (CoRR, 2021)

Sustainable AI: Environmental Implications, Challenges and Opportunities. - 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; et al. (CoRR, 2021)

Asynchronous Gradient Push. - Mahmoud Assran; Michael G. Rabbat (IEEE Trans. Autom. Control., 2021)

Federated Learning with Buffered Asynchronous Aggregation. - John Nguyen; Kshitiz Malik; Hongyuan Zhan; Ashkan Yousefpour; Mike Rabbat; Mani Malek 001; Dzmitry Huba (AISTATS, 2022)

Masked Siamese Networks for Label-Efficient Learning. - Mahmoud Assran; Mathilde Caron; Ishan Misra; Piotr Bojanowski; Florian Bordes; Pascal Vincent; Armand Joulin; Mike Rabbat; Nicolas Ballas (ECCV (31), 2022)

Federated Learning with Partial Model Personalization. - Krishna Pillutla; Kshitiz Malik; Abdelrahman Mohamed; Michael G. Rabbat; Maziar Sanjabi; Lin Xiao 003 (ICML, 2022)

PAPAYA: Practical, Private, and Scalable Federated Learning. - 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; et al. (MLSys, 2022)

Sustainable AI: Environmental Implications, Challenges and Opportunities. - 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; et al. (MLSys, 2022)

Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity. - Kiwan Maeng; Haiyu Lu; Luca Melis; John Nguyen; Mike Rabbat; Carole-Jean Wu (RecSys, 2022)

Privacy-aware compression for federated data analysis. - Kamalika Chaudhuri; Chuan Guo 001; Mike Rabbat (UAI, 2022)

Positive Unlabeled Contrastive Learning. - Anish Acharya; Sujay Sanghavi; Li Jing 001; Bhargav Bhushanam; Dhruv Choudhary; Michael G. Rabbat; Inderjit S. Dhillon (CoRR, 2022)

Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated Learning. - John Nguyen; Kshitiz Malik; Maziar Sanjabi; Michael G. Rabbat (CoRR, 2022)

The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning. - Chuan Guo 001; Kamalika Chaudhuri; Pierre Stock; Mike Rabbat (CoRR, 2022)

FedShuffle: Recipes for Better Use of Local Work in Federated Learning. - Samuel Horváth; Maziar Sanjabi; Lin Xiao 003; Peter Richtárik; Michael G. Rabbat (Trans. Mach. Learn. Res., 2022)

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture. - Mahmoud Assran; Quentin Duval; Ishan Misra; Piotr Bojanowski; Pascal Vincent; Michael G. Rabbat; Yann LeCun; Nicolas Ballas (CVPR, 2023)

The hidden uniform cluster prior in self-supervised learning. - Mido Assran; Randall Balestriero; Quentin Duval; Florian Bordes; Ishan Misra; Piotr Bojanowski; Pascal Vincent; Michael G. Rabbat; Nicolas Ballas (ICLR, 2023)

Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. - John Nguyen; Jianyu Wang; Kshitiz Malik; Maziar Sanjabi; Michael G. Rabbat (ICLR, 2023)

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. - Chuan Guo 001; Kamalika Chaudhuri; Pierre Stock; Michael G. Rabbat (ICML, 2023)

Green Federated Learning. - Ashkan Yousefpour; Shen Guo; Ashish Shenoy; Sayan Ghosh; Pierre Stock; Kiwan Maeng; Schalk-Willem Krüger; Michael G. Rabbat; Carole-Jean Wu; Ilya Mironov (CoRR, 2023)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 2023)

A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale. - Hao-Jun Michael Shi; Tsung-Hsien Lee; Shintaro Iwasaki; Jose Gallego-Posada; Zhijing Li 001; Kaushik Rangadurai; Dheevatsa Mudigere; Michael Rabbat (CoRR, 2023)

lo-fi: distributed fine-tuning without communication. - Mitchell Wortsman; Suchin Gururangan; Shen Li; Ali Farhadi; Ludwig Schmidt; Michael G. Rabbat; Ari S. Morcos (Trans. Mach. Learn. Res., 2023)

Embracing Diversity: Interpretable Zero-shot Classification Beyond One Vector Per Class. - Mazda Moayeri; Michael Rabbat; Mark Ibrahim; Diane Bouchacourt (FAccT, 2024)

The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More. - Ouail Kitouni; Niklas Nolte; Adina Williams; Michael Rabbat; Diane Bouchacourt; Mark Ibrahim (NeurIPS, 2024)

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping. - Lucas Lehnert; Sainbayar Sukhbaatar; Paul McVay; Michael Rabbat; Yuandong Tian (CoRR, 2024)

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning. - Jonathan Lebensold; Maziar Sanjabi; Pietro Astolfi; Adriana Romero-Soriano; Kamalika Chaudhuri; Mike Rabbat; Chuan Guo 001 (CoRR, 2024)

Transformers Can Navigate Mazes With Multi-Step Prediction. - Niklas Nolte; Ouail Kitouni; Adina Williams; Mike Rabbat; Mark Ibrahim (CoRR, 2024)

EvalGIM: A Library for Evaluating Generative Image Models. - 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; et al. (CoRR, 2024)

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning. - Shengbang Tong; David Fan; Jiachen Zhu 002; Yunyang Xiong; Xinlei Chen; Koustuv Sinha; Michael Rabbat; Yann LeCun; Saining Xie; Zhuang Liu 003 (CoRR, 2024)

Revisiting Feature Prediction for Learning Visual Representations from Video. - Adrien Bardes; Quentin Garrido; Jean Ponce; Xinlei Chen; Michael Rabbat; Yann LeCun; Mido Assran; Nicolas Ballas (Trans. Mach. Learn. Res., 2024)

DINOv2: Learning Robust Visual Features without Supervision. - 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; et al. (Trans. Mach. Learn. Res., 2024)

Towards General-Purpose Model-Free Reinforcement Learning. - Scott Fujimoto; Pierluca D'Oro; Amy Zhang 001; Yuandong Tian; Michael Rabbat (ICLR, 2025)

Accelerating neural network training: An analysis of the AlgoPerf competition. - Priya Kasimbeg; Frank Schneider 001; Runa Eschenhagen; Juhan Bae; Chandramouli Shama Sastry; Mark Saroufim; Boyuan Feng; Less Wright; Edward Z. Yang; Zachary Nado; Sourabh Medapati; Philipp Hennig; et al. (ICLR, 2025)

Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces. - DiJia Su; Sainbayar Sukhbaatar; Michael Rabbat; Yuandong Tian; Qinqing Zheng (ICLR, 2025)

Intuitive physics understanding emerges from self-supervised pretraining on natural videos. - Quentin Garrido; Nicolas Ballas; Mahmoud Assran; Adrien Bardes; Laurent Najman; Michael Rabbat; Emmanuel Dupoux; Yann LeCun (CoRR, 2025)

Scaling Language-Free Visual Representation Learning. - David Fan; Shengbang Tong; Jiachen Zhu 002; Koustuv Sinha; Zhuang Liu 003; Xinlei Chen; Michael Rabbat; Nicolas Ballas; Yann LeCun; Amir Bar; Saining Xie (CoRR, 2025)

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D. - Sergio Arnaud; Paul McVay; Ada Martin; Arjun Majumdar; Krishna Murthy Jatavallabhula; Phillip Thomas; Ruslan Partsey; Daniel Dugas; Abha Gejji; Alexander Sax; Vincent-Pierre Berges; Mikael Henaff; et al. (CoRR, 2025)

IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments. - Florian Bordes; Quentin Garrido; Justine T. Kao; Adina Williams; Michael Rabbat; Emmanuel Dupoux (CoRR, 2025)

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning. - Mido Assran; Adrien Bardes; David Fan; Quentin Garrido; Russell Howes; Mojtaba Komeili; Matthew J. Muckley; Ammar Rizvi; Claire Roberts; Koustuv Sinha; Artem Zholus; Sergio Arnaud; et al. (CoRR, 2025)